Virtual power plant adjustable potential quantitative estimation method and system considering carbon flow

Abstract

The invention provides a carbon flow-considered virtual power plant adjustable potential quantitative estimation method and system, and the method comprises the steps: obtaining the value of each index in an adjustable potential index set based on the pre-constructed adjustable potential index set; the value of the unit electric quantity carbon emission index in the adjustable potential index set is determined according to the node network loss power allocation amount of the virtual power plant calculated according to the carbon flow data in the historical operation data; based on a grey target-foreground theory, obtaining an adjustable potential quantized value; according to the method and the system, the unit electric quantity carbon emission index value is obtained by considering the node network loss power allocation amount of the virtual power plant, and the quantification of the adjustable potential of the virtual power plant is realized in combination with the grey target-foreground theory, so that the accuracy and the fairness of the carbon emission responsibility allocation of the virtual power plant are improved, the formulation of a more reasonable demand response strategy is facilitated, and the economic benefit is improved. And under the condition of promoting accurate carbon emission allocation, more reasonable energy optimization configuration is performed on the virtual power plant, the carbon emission of the system is reduced, and the energy utilization efficiency and the low-carbon degree of the virtual power plant are improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of power system optimization scheduling and adjustable potential quantification, and specifically relates to a method and system for quantifying the adjustable potential of a virtual power plant considering carbon flow. Background Art

[0002] In recent years, with the grid connection of a higher proportion of renewable energy, the power system has gradually shifted from the traditional centralized power generation mode to the direction of distributed and renewable energy integration, and virtual power plants have become a new form of power system development. At the same time, the low-carbonization of the power system has received widespread attention from scholars at home and abroad. Therefore, the construction of a multi-dimensional and multi-time scale virtual power plant adjustable potential quantitative evaluation index system considering the carbon flow distribution has become a research hotspot in the field of power system optimization scheduling and potential quantification. Constructing a virtual power plant carbon flow network model, clarifying the carbon emission responsibility sharing of each node in the virtual power plant, and thus establishing a virtual power plant adjustable potential quantitative model, can achieve fair sharing of carbon emission responsibilities of user-side nodes of virtual power plants, improve the willingness of user-side flexible resources to participate in demand response, provide adjustable resource support for subsequent virtual power plant demand response, and help formulate virtual power plant resource optimization allocation strategies.

[0003] However, the existing research on carbon emission responsibility allocation and virtual power plant adjustable potential quantification still has some shortcomings. The existing carbon emission responsibility allocation method is often obtained by converting the primary energy consumption of macro statistics when calculating carbon emissions. Since carbon emissions almost all come from the electricity production link, energy exporting regions are in a weak position in low-carbon development; the existing virtual power plant adjustable potential quantification method often only considers the overall carbon emissions of the system when calculating the low-carbon cost when constructing the potential evaluation index system, but does not go into detail at each load node of the system, which is not enough to support the subsequent virtual power plant demand response considering multi-agent game. Summary of the invention

[0004] In order to overcome the above-mentioned deficiencies of the prior art, the present invention proposes a method for quantitatively estimating the adjustable potential of a virtual power plant considering carbon flow, comprising:

[0005] Based on a pre-constructed set of adjustable potential indicators of a virtual power plant at a set time scale, the value of each indicator in the adjustable potential indicator set is obtained from the historical operation data of the virtual power plant; wherein the historical operation data includes carbon flow data, the adjustable potential indicator set includes a unit electricity carbon emission indicator, and the value of the unit electricity carbon emission indicator is determined according to the node network loss power sharing amount of the virtual power plant calculated according to the carbon flow data;

[0006] Based on the grey target-foreground theory, the values ​​of each indicator are subjected to grey target transformation, and the results after grey target transformation are coefficientized and input into the foreground value function to obtain the positive and negative foreground values ​​of each indicator.

[0007] The positive and negative prospect values ​​of each indicator are weighted and summed to obtain the quantified value of the adjustable potential of the virtual power plant under the set time scale.

[0008] Preferably, the process of determining the value of the carbon emission index per unit of electricity includes:

[0009] According to the reverse flow distribution matrix considering the network loss in the carbon flow data, the total branch power of the network branch of the virtual power plant is obtained as the total network loss of the virtual power plant;

[0010] Based on the adjustable network loss allocation coefficient, the total network loss of the virtual power plant is allocated to each network node of the virtual power plant to obtain the node network loss power allocation amount of each network node;

[0011] Based on each of the network nodes, the node carbon emissions of the network node are divided by the sum of the injected power of the network node and the node network loss power share to obtain the node carbon footprint intensity of the network node as the value of the carbon emission index per unit electricity.

[0012] Preferably, the obtaining of the total branch power of the network branches of the virtual power plant as the total network loss of the virtual power plant according to the reverse flow distribution matrix considering the network loss in the carbon flow data includes:

[0013] The network branches of the virtual power plant are used as virtual nodes, and together with the various network nodes, as a master node; based on the carbon flow data, a reverse flow distribution matrix of the master node considering network loss and a master node injection power matrix are obtained;

[0014] The inverse matrix of the countercurrent distribution matrix is ​​multiplied by the total node injection power matrix, and the elements corresponding to the virtual nodes in the resultant matrix are added to obtain the total branch power of the network branches of the virtual power plant as the total network loss of the virtual power plant.

[0015] Preferably, the process of obtaining the node network loss power sharing amount satisfies the following expression:

[0016]

[0017] in, is the node network loss power sharing amount of network node k, k = 1,...,n, n is the total number of network nodes, is the injected power of network node k, P 总网损 is the total network loss of the virtual power plant, and β is the adjustable network loss allocation coefficient.

[0018] Preferably, the process of obtaining the node carbon trace intensity satisfies the following expression:

[0019]

[0020] Among them, F k is the node carbon trace intensity of network node k, is the node carbon emission of network node k, is the node network loss power allocation of network node k, is the injected power of network node k.

[0021] Preferably, based on the grey target-foreground theory, the values ​​of each indicator are subjected to grey target transformation, the results after grey target transformation are coefficientized and input into the foreground value function to obtain the positive and negative foreground values ​​of each indicator, including:

[0022] In a typical scenario of a virtual power plant, an initial evaluation matrix is ​​constructed based on the values ​​of each indicator in the adjustable potential indicator set, wherein a matrix element in the initial evaluation matrix corresponds to the value of a certain indicator of a certain network node of the virtual power plant;

[0023] According to the positive and negative correlation between each indicator and the adjustable potential of the virtual power plant, a standard sequence of the initial evaluation matrix is ​​obtained, wherein each sequence element in the standard sequence corresponds to a standardized result of the value of each indicator;

[0024] Based on each of the matrix elements, the matrix element and the sequence element corresponding to its index are input into a grey-target transformation formula to obtain a standard value of the matrix element;

[0025] Inputting the standard value of the matrix element into the bull's eye coefficient calculation formula to obtain the positive and negative bull's eye coefficients of the matrix element;

[0026] The positive and negative bull's-eye coefficients of the matrix elements are input into the prospect value function to obtain the positive and negative prospect values ​​of the matrix elements, that is, the positive and negative prospect values ​​of the indicators of the network nodes corresponding to the matrix elements.

[0027] Preferably, the grey target transformation formula is expressed as:

[0028]

[0029] Where T(·) represents the grey target transformation operation formula, d kj is the value of the jth index of the kth network node in the initial evaluation matrix, r kj is the standard value of the jth indicator of the kth network node; is the positive bull's-eye normalized result of the value of the jth indicator. When the jth indicator is positively correlated with the adjustable potential of the virtual power plant, When the jth indicator is negatively correlated with the adjustable potential of the virtual power plant,

[0030] Preferably, the bull's eye coefficient calculation formula is expressed as:

[0031]

[0032] in, is the positive bullseye coefficient of the value of the jth indicator of the kth network node, is the negative bull's eye coefficient of the value of the jth index of the kth network node, ρ is the resolution coefficient, is the grey correlation difference between the positive bull's eye and the standard value of the jth index of the kth network node, is the grey correlation difference between the negative bull's eye and the standard value of the jth indicator of the kth network node, r kj is the standard value of the jth indicator of the kth network node.

[0033] Preferably, the typical scenarios of the virtual power plant include typical scenarios of the virtual power plant during peak periods, typical scenarios during normal periods, and typical scenarios during valley periods, and different typical scenarios of the virtual power plant correspond to different quantitative values ​​of adjustable potential.

[0034] Preferably, the weighted summing of the positive and negative prospect values ​​of each indicator to obtain the quantified value of the adjustable potential of the virtual power plant at the set time scale includes:

[0035] Based on prospect theory, obtain the benefit decision weight and risk decision weight of each indicator;

[0036] Based on each network node of the virtual power plant, the benefit decision weight of each indicator of the network node is weighted to the positive prospect value of each indicator, and the risk decision weight of each indicator of the network node is weighted to the negative prospect value of each indicator. The weighted results are summed to obtain the adjustable potential quantification value of the network node at the set time scale; the adjustable potential quantification value of all network nodes at the set time scale is the adjustable potential quantification value of the virtual power plant at the set time scale.

[0037] Preferably, the adjustable potential indicator set under the set time scale includes one or more of the following:

[0038] Maintenance load index, maintenance rate index, maintenance cost index, unit electricity output value index, unit electricity tax index and unit electricity carbon emission index on a monthly time scale;

[0039] Weekly rest load index, load reduction rate index, rotation cost index, unit electricity output value index, unit electricity tax index and unit electricity carbon emission index on a weekly time scale;

[0040] The staggered load indicators, staggered electricity consumption proportion indicators, staggered cost indicators, unit electricity output value indicators, unit electricity tax indicators and unit electricity carbon emission indicators on a daily time scale.

[0041] Based on the same inventive concept, the present invention also provides a system for quantitatively estimating the adjustable potential of a virtual power plant taking carbon flow into consideration, comprising:

[0042] An indicator value acquisition module, for obtaining the value of each indicator in the adjustable potential indicator set from the historical operation data of the virtual power plant based on the pre-constructed adjustable potential indicator set of the virtual power plant at a set time scale; wherein the historical operation data includes carbon flow data, the adjustable potential indicator set includes a unit electricity carbon emission indicator, and the value of the unit electricity carbon emission indicator is determined according to the node network loss power sharing amount of the virtual power plant calculated according to the carbon flow data;

[0043] The first calculation module is used to perform gray target transformation on the value of each indicator based on the gray target-foreground theory, and input the result after the gray target transformation into the foreground value function after coefficientization to obtain the positive and negative foreground value of each indicator;

[0044] The second calculation module is used to weight the positive and negative prospect values ​​of each indicator to obtain a quantitative value of the adjustable potential of the virtual power plant under the set time scale.

[0045] Preferably, the indicator value acquisition module includes:

[0046] The first submodule is used to obtain the total branch power of the network branch of the virtual power plant as the total network loss of the virtual power plant according to the reverse flow distribution matrix considering the network loss in the carbon flow data;

[0047] The second submodule is used to allocate the total network loss of the virtual power plant to each network node of the virtual power plant based on the adjustable network loss allocation coefficient, and obtain the node network loss power allocation amount of each network node;

[0048] The third submodule is used to divide the node carbon emissions of the network node by the sum of the injected power of the network node and the node network loss power share based on each of the network nodes, to obtain the node carbon footprint intensity of the network node as the value of the carbon emission index per unit electricity.

[0049] Preferably, the first submodule is specifically used for:

[0050] The network branches of the virtual power plant are used as virtual nodes, and together with the various network nodes, as a master node; based on the carbon flow data, a reverse flow distribution matrix of the master node considering network loss and a master node injection power matrix are obtained;

[0051] The inverse matrix of the countercurrent distribution matrix is ​​multiplied by the total node injection power matrix, and the elements corresponding to the virtual nodes in the resultant matrix are added to obtain the total branch power of the network branches of the virtual power plant as the total network loss of the virtual power plant.

[0052] Preferably, the process of obtaining the node network loss power sharing amount satisfies the following expression:

[0053]

[0054] in, is the node network loss power sharing amount of network node k, k = 1,...,n, n is the total number of network nodes, is the injected power of network node k, P 总网损 is the total network loss of the virtual power plant, and β is the adjustable network loss allocation coefficient.

[0055] Preferably, the process of obtaining the node carbon trace intensity satisfies the following expression:

[0056]

[0057] Among them, F k is the node carbon trace intensity of network node k, is the node carbon emission of network node k, is the node network loss power allocation of network node k, is the injected power of network node k.

[0058] Preferably, the first calculation module is specifically used for:

[0059] In a typical scenario of a virtual power plant, an initial evaluation matrix is ​​constructed based on the values ​​of each indicator in the adjustable potential indicator set, wherein a matrix element in the initial evaluation matrix corresponds to the value of a certain indicator of a certain network node of the virtual power plant;

[0060] According to the positive and negative correlation between each indicator and the adjustable potential of the virtual power plant, a standard sequence of the initial evaluation matrix is ​​obtained, wherein each sequence element in the standard sequence corresponds to a standardized result of the value of each indicator;

[0061] Based on each of the matrix elements, the matrix element and the sequence element corresponding to its index are input into a grey-target transformation formula to obtain a standard value of the matrix element;

[0062] Inputting the standard value of the matrix element into the bull's eye coefficient calculation formula to obtain the positive and negative bull's eye coefficients of the matrix element;

[0063] The positive and negative bull's-eye coefficients of the matrix elements are input into the prospect value function to obtain the positive and negative prospect values ​​of the matrix elements, that is, the positive and negative prospect values ​​of the indicators of the network nodes corresponding to the matrix elements.

[0064] Preferably, the grey target transformation formula is expressed as:

[0065]

[0066] Where T(·) represents the grey target transformation operation formula, d kj is the value of the jth index of the kth network node in the initial evaluation matrix, r kj is the standard value of the jth indicator of the kth network node; is the positive bull's-eye normalized result of the value of the jth indicator. When the jth indicator is positively correlated with the adjustable potential of the virtual power plant, When the jth indicator is negatively correlated with the adjustable potential of the virtual power plant,

[0067] Preferably, the bull's eye coefficient calculation formula is expressed as:

[0068]

[0069] in, is the positive bullseye coefficient of the value of the jth indicator of the kth network node, is the negative bull's eye coefficient of the value of the jth index of the kth network node, ρ is the resolution coefficient, is the grey correlation difference between the positive bull's eye and the standard value of the jth index of the kth network node, is the grey correlation difference between the negative bull's eye and the standard value of the jth indicator of the kth network node, r kj is the standard value of the jth indicator of the kth network node.

[0070] Preferably, the typical scenarios of the virtual power plant include typical scenarios of the virtual power plant during peak periods, typical scenarios during normal periods, and typical scenarios during valley periods, and different typical scenarios of the virtual power plant correspond to different quantitative values ​​of adjustable potential.

[0071] Preferably, the second calculation module is specifically used for:

[0072] Based on prospect theory, obtain the benefit decision weight and risk decision weight of each indicator;

[0073] Based on each network node of the virtual power plant, the benefit decision weight of each indicator of the network node is weighted to the positive prospect value of each indicator, and the risk decision weight of each indicator of the network node is weighted to the negative prospect value of each indicator. The weighted results are summed to obtain the adjustable potential quantification value of the network node at the set time scale; the adjustable potential quantification value of all network nodes at the set time scale is the adjustable potential quantification value of the virtual power plant at the set time scale.

[0074] Preferably, the adjustable potential indicator set under the set time scale includes one or more of the following:

[0075] Maintenance load index, maintenance rate index, maintenance cost index, unit electricity output value index, unit electricity tax index and unit electricity carbon emission index on a monthly time scale;

[0076] Weekly rest load index, load reduction rate index, rotation cost index, unit electricity output value index, unit electricity tax index and unit electricity carbon emission index on a weekly time scale;

[0077] The staggered load indicators, staggered electricity consumption proportion indicators, staggered cost indicators, unit electricity output value indicators, unit electricity tax indicators and unit electricity carbon emission indicators on a daily time scale.

[0078] Based on the same inventive concept, the present invention also provides a computer device, including: one or more processors;

[0079] A memory for storing one or more programs;

[0080] When the one or more programs are executed by the one or more processors, a method for quantitatively estimating the adjustable potential of a virtual power plant taking carbon flow into consideration as described above is implemented.

[0081] Based on the same inventive concept, the present invention also provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed, it implements a method for quantitatively estimating the adjustable potential of a virtual power plant taking into account carbon flow as described above.

[0082] Compared with the closest prior art, the present invention has the following beneficial effects:

[0083] The present invention provides a method and system for quantitatively estimating the adjustable potential of a virtual power plant taking carbon flow into consideration, including a set of adjustable potential indicators of a virtual power plant at a set time scale based on a pre-constructed system, and obtaining the value of each indicator in the adjustable potential indicator set from historical operation data of the virtual power plant; wherein the historical operation data includes carbon flow data, and the adjustable potential indicator set includes a carbon emission indicator per unit of electricity, and the value of the carbon emission indicator per unit of electricity is determined according to the node network loss power sharing amount of the virtual power plant calculated based on the carbon flow data; based on the grey target-foreground theory, grey target transformation is performed on the value of each indicator respectively, and the result after grey target transformation is coefficientized and input into the prospect value function to obtain the positive value of each indicator. Negative prospect value; weighted sum of the positive and negative prospect values ​​of each indicator to obtain the quantified value of the adjustable potential of the virtual power plant under the set time scale; the method and system obtain the carbon emission index value per unit electricity volume by considering the node network loss power share of the virtual power plant, and then calculate the positive and negative prospect value of each indicator in combination with the grey target-prospect theory, so as to realize the quantification of the adjustable potential of the virtual power plant, improve the accuracy and fairness of the carbon emission responsibility allocation for each eligible node of the virtual power plant, help to formulate a more reasonable demand response strategy, promote more reasonable energy optimization configuration of virtual power plants under precise carbon emission allocation, reduce system carbon emissions, and improve the energy utilization efficiency and low carbonization degree of virtual power plants. BRIEF DESCRIPTION OF THE DRAWINGS

[0084] Figure 1 A schematic flow chart of a method for quantitatively estimating the adjustable potential of a virtual power plant considering carbon flow provided by the present invention;

[0085] Figure 2 A schematic flow chart of a method for analyzing adjustable potential influencing factors based on principal component analysis provided by the present invention;

[0086] Figure 3 A schematic flow chart of a method for obtaining an optimal clustering result of a typical scenario set of a virtual power plant provided by the present invention;

[0087] Figure 4 A schematic diagram of the structure of a virtual power plant adjustable potential quantitative estimation system considering carbon flow provided by the present invention;

[0088] Figure 5 The present invention provides a schematic structural diagram of an electronic device. DETAILED DESCRIPTION

[0089] The specific implementation modes of the present invention are further described in detail below with reference to the accompanying drawings.

[0090] Embodiment 1:

[0091] The present invention provides a method for quantitatively estimating the adjustable potential of a virtual power plant considering carbon flow, such as Figure 1 As shown, including:

[0092] S1. Based on the pre-built adjustable potential indicator set of the virtual power plant at a set time scale, the value of each indicator in the adjustable potential indicator set is obtained from the historical operation data of the virtual power plant; wherein the historical operation data includes carbon flow data, the adjustable potential indicator set includes a unit electricity carbon emission indicator, and the value of the unit electricity carbon emission indicator is determined according to the node network loss power allocation of the virtual power plant calculated according to the carbon flow data;

[0093] S2. Based on the grey target-foreground theory, the values ​​of each indicator are subjected to grey target transformation, the results after grey target transformation are coefficientized and input into the foreground value function to obtain the positive and negative foreground values ​​of each indicator;

[0094] S3. Take the weighted sum of the positive and negative prospect values ​​of each indicator to obtain the quantitative value of the adjustable potential of the virtual power plant under the set time scale.

[0095] This method obtains the carbon emission index value per unit of electricity by considering the system carbon flow relationship and the node network loss power sharing of the virtual power plant, and then combines the grey target-prospect theory to calculate the positive and negative prospect values ​​of each indicator, thereby realizing dynamic estimation of the regulation potential of the virtual power plant, improving the accuracy and fairness of the carbon emission responsibility sharing of the virtual power plant, helping to formulate more reasonable demand response strategies, and promoting more reasonable energy optimization configuration of virtual power plants under precise carbon emission sharing, reducing system carbon emissions, and improving the energy utilization efficiency and low-carbonization level of virtual power plants.

[0096] Taking into account the multi-time scale and multi-dimensional characteristics of the operating data of the virtual power plant, in the above S1, by pre-constructing an adjustable potential indicator set under a set time scale, the final adjustable potential quantification value can reflect the multi-time scale and multi-dimensional characteristics, so that the adjustable potential quantification value can have more practical application value.

[0097] In S1, the pre-construction process of the adjustable potential indicator set may include:

[0098] Analysis of factors affecting the adjustable potential of virtual power plants based on principal component analysis: Based on the operating characteristics of virtual power plants, the main factors affecting the adjustable potential of virtual power plants are studied through principal component analysis. The flowchart of the analysis of factors affecting the adjustable potential based on principal component analysis is shown in Figure 2 , where X is the data set of adjustable potential influencing factors consisting of all adjustable potential influencing factors in the virtual power plant, and the station area is the power supply range or area of ​​a transformer.

[0099] After obtaining the main influencing factors affecting the flexible load regulation potential of the substation from the data set through principal component analysis, a virtual power plant adjustable potential index set is constructed according to the main influencing factors, that is, a multi-dimensional and multi-time scale virtual power plant adjustable potential evaluation index system is constructed, and the adjustable potential index sets under different set time scales are different: This application mainly studies the peak avoidance potential of virtual power plants when participating in demand response, and divides the peak avoidance potential of virtual power plants into maintenance potential Y1 on a monthly time scale, rotation potential Y2 on a weekly time scale, and staggered potential Y3 on a daily time scale.

[0100] The maintenance potential Y1, rotation potential Y2, and staggered potential Y3 indicators of the virtual power plant are composed of 6 sub-indicators, including 3 characteristic indicators (Xkt1-Xkt3) and 3 static indicators (Xkt4-Xkt6). The distribution of kt = 1, 2, 3 represents maintenance, rotation, and staggered. The specific maintenance, rotation, and staggered potential evaluation index table is shown in Table 1. Xktj in the table is an adjustable potential indicator.

[0101] Specifically, the adjustable potential indicator set under the set time scale is shown in Table 1:

[0102] Table 1 Adjustable potential index table under the virtual power plant setting time scale

[0103]

[0104] Except for the value of the carbon emission index per unit of electricity, other index values ​​can be directly obtained through historical operation data or through simple calculation.

[0105] Considering that there are three typical scenarios of peak, flat and valley when virtual power plants are running, the adjustable potentials in different typical scenarios vary greatly. If the historical operation data of the whole year is directly used to obtain the index value, the final adjustable potential quantification value will not have practical application value. Therefore, in this embodiment, the typical scenarios of virtual power plants include typical scenarios of virtual power plant peak periods, typical scenarios of flat periods, and typical scenarios of valley periods. Different adjustable potential quantification values ​​are obtained according to the historical operation data of different typical scenarios of virtual power plants, that is, the maintenance potential quantification value, rotation potential quantification value, and staggered potential quantification value in the typical scenario of peak periods, the maintenance potential quantification value, rotation potential quantification value, and staggered potential quantification value in the typical scenario of flat periods, and the maintenance potential quantification value, rotation potential quantification value, and staggered potential quantification value in the typical scenario of valley periods are finally obtained, a total of nine adjustable potential quantification value results, making the potential quantification evaluation results more accurate.

[0106] Specifically, based on the fuzzy C-means clustering algorithm, taking into account seasonal and peak-valley differences, typical scenarios of virtual power plants are generated: first, typical operation days of virtual power plants are selected in the four seasons of the year, and then each typical day is divided into peak, flat and valley periods according to the user-side load demand. Finally, the fuzzy C-means clustering algorithm is used to generate typical scenarios of virtual power plants.

[0107] The fuzzy C-means clustering algorithm obtains the optimal clustering result of the typical scenario set of the virtual power plant through iterative optimization of the cluster center Z and the membership matrix U. The specific steps are as follows, and the flow chart is as follows: Figure 3 shown.

[0108] (1) Sampling and analyzing the power data of the source and load sides of the virtual power plant on a typical day, the original scenes of the peak, flat and valley periods of the virtual power plant are established with the sampling time as the sequence, that is, the membership matrix U is initialized:

[0109]

[0110] In the formula, They represent the original scenes of the virtual power plant during peak, flat and valley periods respectively; Respectively represent the source load power of the g-th sampling point at sampling time t in the peak, flat and valley periods, g = 1, 2, ..., k n , k n is the total number of sampling points; T top , T lev , T low Represent the total number of sampling moments in the peak, flat and valley periods of the virtual power plant respectively.

[0111] (2) Set the iteration termination threshold and fuzzy parameter, set the initial cluster center Z, and cluster the samples into K n A cluster.

[0112] (3) Calculate the membership matrix U and update the cluster center Z of the virtual power plant typical scenario based on the membership matrix U. If the cluster center matrix Z of the typical scenario after the b+1th iteration is b+1 and the typical scene cluster center matrix Z after the bth iteration b If the iteration termination condition is met, the iteration is stopped, otherwise it returns to step (2) to continue the iteration. The iteration termination condition is expressed as:

[0113] ||Z b+1 -Z b ||<ε,

[0114] In the formula, ε is the iteration termination threshold.

[0115] (4) Output typical scene K during peak period top , Typical scenes during normal times K lev , Typical scenes during valley hours Klow and the probability of the scenario occurring.

[0116] The daily load curves of typical scenarios are summarized by month and week, and the monthly and weekly average loads, peak and valley loads and other parameters are calculated. The values ​​of indicators such as the maintenance rate index and the load reduction rate index at monthly and weekly time scales are analyzed.

[0117] Considering the fairness of carbon emission responsibility sharing, the carbon emission responsibility sharing based on node carbon footprint strength introduces an adjustable network loss allocation coefficient to distribute the carbon emissions corresponding to the network loss to the network nodes in the actual topology network of the substation. Then, the node carbon footprint strength of each network node in the virtual power plant is determined based on the carbon flow theory.

[0118] Specifically, assuming that there are n network nodes and m branches in the substation topology network, and the branches are regarded as virtual nodes, the power flowing through the virtual nodes is the branch power. For any total node k', k'=1,...,m+n, there is a power flow equation:

[0119]

[0120] Right now:

[0121]

[0122] In matrix form, we have:

[0123]

[0124] In the formula is the external injection power of the total node k', is the total power flowing through the total node k', p k'q is the transfer power between the total node k' and its different nodes q, q = 1, ..., m + n, is the runoff power of node q.

[0125] Right now:

[0126]

[0127] Where P 注入 The total node injection power matrix, P 流经 is the total node power matrix, is the countercurrent distribution matrix considering network loss at the total node, and its matrix elements are:

[0128]

[0129] Among them, P mn is the first active power of the nth network node flowing into branch mn, P nis the total injected or outflow power of the nth network node, Γ m is the incoming line set of virtual node m.

[0130] Through the above derivation process, it can be concluded that after obtaining the reverse flow distribution matrix of the total node considering the network loss and the total node injection power matrix, the total node flow power matrix can be obtained.

[0131] Therefore, in the above S1, the process of determining the value of the carbon emission index per unit of electricity may include:

[0132] According to the reverse flow distribution matrix considering the network loss in the carbon flow data, the total branch power of the network branch of the virtual power plant is obtained as the total network loss of the virtual power plant;

[0133] Based on the adjustable network loss allocation coefficient, the total network loss of the virtual power plant is allocated to each network node of the virtual power plant to obtain the node network loss power allocation of each network node;

[0134] Based on each network node, the node carbon emissions of the network node are divided by the sum of the injected power of the network node and the node network loss power share to obtain the node carbon footprint intensity of the network node, which is used as the value of the carbon emission indicator per unit electricity.

[0135] Among them, according to the reverse flow distribution matrix considering network loss in the carbon flow data, the total branch power of the network branch of the virtual power plant is obtained as the total network loss of the virtual power plant, including: taking the network branch of the virtual power plant as a virtual node and taking each network node as a total node; according to the carbon flow data, obtaining the reverse flow distribution matrix considering network loss of the total node and the total node injection power matrix;

[0136] The inverse matrix of the reverse flow distribution matrix is ​​multiplied by the total node injection power matrix, and the elements corresponding to the virtual nodes in the total node flow power matrix are added to obtain the total branch power of the network branch of the virtual power plant, which is used as the total network loss P of the virtual power plant. 总网损 , expressed as;

[0137]

[0138] In this embodiment, the process of obtaining the node network loss power sharing amount satisfies the following expression:

[0139]

[0140] in, is the node network loss power sharing amount of network node k, k = 1,...,n, n is the total number of network nodes, is the injected power of network node k, P 总网损 is the total network loss of the virtual power plant, and β is the adjustable network loss allocation coefficient.

[0141] In this embodiment, the process of obtaining the node carbon trace intensity satisfies the following expression:

[0142]

[0143] Among them, F k is the node carbon trace intensity of network node k, is the node carbon emission of network node k, is the node network loss power allocation of network node k, is the injected power of network node k.

[0144] Taking into account the dimensional and numerical differences of each indicator in the adjustable potential indicator set, in the above S2, the positive and negative bull's eye coefficient matrix is ​​constructed by the grey target theory in each typical scenario of the virtual power plant, and the acceptable value range of each indicator is given in advance to realize the grey target transformation of the virtual power plant staggered adjustable potential evaluation matrix, thereby unifying the dimensional and numerical range of different indicators. The core of the grey target theory is to quantify the adjustable potential of the virtual power plant by constructing a standard pattern sequence, namely the bull's eye, and obtaining the bull's eye coefficient.

[0145] Specifically, the above S2 includes:

[0146] S201. In a typical scenario of a virtual power plant, an initial evaluation matrix is ​​constructed based on the values ​​of each indicator in the adjustable potential indicator set, wherein a matrix element in the initial evaluation matrix corresponds to the value of a certain indicator of a certain network node of the virtual power plant;

[0147] S202, according to the positive and negative correlation between each indicator and the adjustable potential of the virtual power plant, a standard sequence of the initial evaluation matrix is ​​obtained, and each sequence element in the standard sequence corresponds to the standardized result of the value of each indicator;

[0148] Specifically, the maintenance potential, rotation potential and staggered potential are calculated independently. Taking the staggered potential of the virtual power plant as an example, the initial evaluation matrix of the staggered potential of the virtual power plant is assumed to be D, D = (d kj ) n×6 , n is the number of network nodes, k∈(0,n), j∈(0,6), d kj is the value of the jth index of the kth network node in the initial evaluation matrix. Considering the different types of indicators, a standard sequence is constructed:

[0149] Effectiveness indicators:

[0150] Cost indicators:

[0151] In the formula, and Represent the positive and negative bullseye of the benefit indicator respectively. and They represent the positive and negative bull's-eye of cost-based indicators respectively.

[0152] Benefit-type indicators refer to indicators whose values ​​are positively correlated with adjustable potential. Feature indicators 1 and 2 in Table 1 are benefit-type indicators. Cost-type indicators refer to indicators whose values ​​are negatively correlated with adjustable potential. Feature indicator 3 and the three static indicators in Table 1 are all cost-type indicators.

[0153] By calculating each index, the standard sequence of the initial evaluation matrix D can be obtained:

[0154] in, is the positive bull's eye standard sequence, is the positive bull's eye standard sequence, is the positive bullseye normalization result of the value of the jth indicator, is the negative bull's-eye normalization result of the value of the j-th indicator, and the j-th indicator is a benefit-type indicator or a cost-type indicator;

[0155] S203, based on each matrix element, inputting the matrix element and the sequence element corresponding to its index into the gray-target transformation formula to obtain the standard value of the matrix element;

[0156] In this embodiment, the gray target transformation formula is expressed as:

[0157]

[0158] Where T(·) represents the grey target transformation operation formula, d kj is the value of the jth index of the kth network node in the initial evaluation matrix, r kj is the standard value of the jth indicator of the kth network node; is the positive bull's-eye normalized result of the value of the jth indicator. When the jth indicator is positively correlated with the adjustable potential of the virtual power plant, When the jth indicator is negatively correlated with the adjustable potential of the virtual power plant,

[0159] S204, inputting the standard value of the matrix element into the bull's eye coefficient calculation formula to obtain the positive and negative bull's eye coefficients of the matrix element, and each positive and negative bull's eye coefficient constitutes a positive and negative bull's eye coefficient matrix;

[0160] In this embodiment, the calculation formula of the bull's eye coefficient is expressed as:

[0161]

[0162] in, is the positive bullseye coefficient of the value of the jth indicator of the kth network node, is the negative bull's eye coefficient of the value of the jth index of the kth network node, ρ is the resolution coefficient, is the grey correlation difference between the positive bull's eye and the standard value of the jth index of the kth network node, is the grey correlation difference between the negative bull's eye and the standard value of the jth indicator of the kth network node, r kj is the standard value of the jth indicator of the kth network node.

[0163] S205. Input the positive and negative bull's-eye coefficients of the matrix elements into the prospect value function to obtain the positive and negative prospect values ​​of the matrix elements, that is, the positive and negative prospect values ​​of the indicators of the network nodes corresponding to the matrix elements.

[0164] In this embodiment, based on the positive and negative bull's-eye coefficients of the virtual power plant's time-shifting potential obtained by the grey target theory in S204, a positive and negative prospect value function of the jth indicator of the virtual power plant's time-shifting potential for the user-side load node k is constructed:

[0165]

[0166] In the formula, is the positive prospect value of the jth indicator of the kth network node, is the negative prospect value of the jth indicator of the kth network node, α and μ represent the degree of concavity of the positive and negative prospect value functions of the virtual power plant respectively; θ is the sensitivity coefficient of profit and loss.

[0167] The above S3 includes:

[0168] Based on prospect theory, obtain the benefit decision weight and risk decision weight of each indicator;

[0169] Based on each network node of the virtual power plant, the benefit decision weight of each indicator of the network node is weighted to the positive prospect value of each indicator, and the risk decision weight of each indicator of the network node is weighted to the negative prospect value of each indicator. The weighted results are summed to obtain the adjustable potential quantitative value of the network node at the set time scale; the adjustable potential quantitative value of all network nodes at the set time scale is the adjustable potential quantitative value of the virtual power plant at the set time scale.

[0170] In this embodiment, according to prospect theory, the gain decision weight and the loss decision weight are defined as:

[0171]

[0172] In the formula, π + (w j ) is the profit decision weight, π - (w j ) is the loss decision weight or risk decision weight; w jis the weight of the jth indicator in the adjustable potential indicator set of the virtual power plant's time-shifting potential; η + With η - They are the risk attitude coefficient for gains and the risk attitude coefficient for losses, respectively.

[0173] The time-staggered potential V of the kth network node of the virtual power plant is k It can be expressed as:

[0174]

[0175] In order to solve the problem that the existing carbon emission responsibility sharing mechanism cannot achieve fair sharing of carbon emission responsibility on the load side, the present invention considers the equivalent carbon emissions caused by network losses, and calculates the carbon emission responsibility sharing per unit electricity of each node on the load side by constructing a countercurrent distribution matrix.

[0176] The present invention analyzes the influencing factors of the adjustable potential of virtual power plants based on the principal component analysis method, and divides the peak-avoidance potential of virtual power plants into maintenance potential on a monthly time scale, rotation potential on a weekly time scale, and staggered potential on a daily time scale, and establishes a multi-time scale and multi-dimensional virtual power plant adjustable potential evaluation index system composed of characteristic indicators and static indicators.

[0177] The present invention constructs a typical scenario of a virtual power plant through a fuzzy C-means clustering algorithm. Based on the grey target theory, a positive and negative bull's-eye coefficient matrix is ​​constructed in the typical scenario of a virtual power plant, and based on the prospect theory, a decision model considering risk loss and benefit is constructed, and the regulation potential is defined as the sum of the positive prospect value and the negative prospect value.

[0178] The carbon emission responsibility sharing mechanism for virtual power plants based on node carbon footprint intensity sharing proposed in the present invention takes into account the additional carbon emissions caused by network losses, determines the node carbon footprint intensity of each network node in the system by constructing a countercurrent distribution matrix, and calculates the carbon emission responsibility sharing amount of each network node on the load side according to the power flowing through the node, so as to obtain accurate node carbon footprint intensity constraints; the multi-dimensional and multi-time scale virtual power plant adjustable potential evaluation index system proposed in the present invention analyzes the influencing factors of the adjustable potential of virtual power plants based on the principal component analysis method, and realizes the accurate calculation of the carbon emission per unit electricity in the quantitative evaluation index based on the obtained node carbon emission responsibility sharing amount; the virtual power plant adjustable potential quantification model based on the grey target-prospect theory proposed in the present invention generates typical energy consumption scenarios of virtual power plants through the fuzzy C-means clustering algorithm, taking into account seasonal and peak-valley differences, and quantifies the regulation potential of virtual power plants under typical scenarios.

[0179] Embodiment 2:

[0180] Based on the same inventive concept, the present invention also provides a virtual power plant adjustable potential quantitative estimation system considering carbon flow, such as Figure 4 As shown, including:

[0181] An indicator value acquisition module is used to obtain the value of each indicator in the adjustable potential indicator set from the historical operation data of the virtual power plant based on the pre-built adjustable potential indicator set of the virtual power plant at a set time scale; wherein the historical operation data includes carbon flow data, the adjustable potential indicator set includes a unit electricity carbon emission indicator, and the value of the unit electricity carbon emission indicator is determined according to the node network loss power allocation of the virtual power plant calculated based on the carbon flow data;

[0182] The first calculation module is used to perform gray target transformation on the value of each indicator based on the gray target-foreground theory, and input the result after the gray target transformation into the foreground value function after coefficientization to obtain the positive and negative foreground value of each indicator;

[0183] The second calculation module is used to weight the positive and negative prospect values ​​of each indicator to obtain the quantified value of the adjustable potential of the virtual power plant under a set time scale.

[0184] In this embodiment, the indicator value acquisition module includes:

[0185] The first submodule is used to obtain the total branch power of the network branches of the virtual power plant as the total network loss of the virtual power plant according to the reverse flow distribution matrix considering the network loss in the carbon flow data;

[0186] The second submodule is used to allocate the total network loss of the virtual power plant to each network node of the virtual power plant based on the adjustable network loss allocation coefficient, and obtain the node network loss power allocation amount of each network node;

[0187] The third submodule is used to divide the node carbon emissions of the network node by the sum of the injected power of the network node and the node network loss power share based on each network node, to obtain the node carbon footprint intensity of the network node as the value of the carbon emission indicator per unit electricity.

[0188] In this embodiment, the first submodule is specifically used for:

[0189] The network branches of the virtual power plant are taken as virtual nodes, and together with each network node, they are taken as the master node; based on the carbon flow data, the reverse flow distribution matrix of the master node considering the network loss and the master node injection power matrix are obtained;

[0190] The inverse matrix of the reverse flow distribution matrix is ​​multiplied by the total node injection power matrix, and the elements corresponding to the virtual nodes in the result matrix of the multiplication are added to obtain the total branch power of the network branches of the virtual power plant as the total network loss of the virtual power plant.

[0191] In this embodiment, the process of obtaining the node network loss power sharing amount satisfies the following expression:

[0192]

[0193] in, is the node network loss power sharing amount of network node k, k = 1,...,n, n is the total number of network nodes, is the injected power of network node k, P 总网损 is the total network loss of the virtual power plant, and β is the adjustable network loss allocation coefficient.

[0194] In this embodiment, the process of obtaining the node carbon trace intensity satisfies the following expression:

[0195]

[0196] Among them, F k is the node carbon trace intensity of network node k, is the node carbon emission of network node k, is the node network loss power allocation of network node k, is the injected power of network node k.

[0197] In this embodiment, the first calculation module is specifically used for:

[0198] In a typical scenario of a virtual power plant, an initial evaluation matrix is ​​constructed based on the values ​​of each indicator in the adjustable potential indicator set. A certain matrix element in the initial evaluation matrix corresponds to the value of a certain indicator of a certain network node of the virtual power plant.

[0199] According to the positive and negative correlation between each indicator and the adjustable potential of the virtual power plant, a standard sequence of the initial evaluation matrix is ​​obtained, and each sequence element in the standard sequence corresponds to the standardized result of the value of each indicator;

[0200] Based on each matrix element, the matrix element and the sequence element corresponding to its index are input into the gray-target transformation formula to obtain the standard value of the matrix element;

[0201] Input the standard value of the matrix element into the bull's eye coefficient calculation formula to obtain the positive and negative bull's eye coefficients of the matrix element;

[0202] The positive and negative bull's-eye coefficients of the matrix elements are input into the prospect value function to obtain the positive and negative prospect values ​​of the matrix elements, that is, the positive and negative prospect values ​​of the indicators of the network nodes corresponding to the matrix elements.

[0203] In this embodiment, the gray target transformation formula is expressed as:

[0204]

[0205] Where T(·) represents the grey target transformation operation formula, d kj is the value of the jth index of the kth network node in the initial evaluation matrix, r kj is the standard value of the jth indicator of the kth network node; is the positive bull's-eye normalized result of the value of the jth indicator. When the jth indicator is positively correlated with the adjustable potential of the virtual power plant, When the jth indicator is negatively correlated with the adjustable potential of the virtual power plant,

[0206] In this embodiment, the calculation formula of the bull's eye coefficient is expressed as:

[0207]

[0208] in, is the positive bullseye coefficient of the value of the jth indicator of the kth network node, is the negative bull's eye coefficient of the value of the jth index of the kth network node, ρ is the resolution coefficient, is the grey correlation difference between the positive bull's eye and the standard value of the jth index of the kth network node, is the grey correlation difference between the negative bull's eye and the standard value of the jth indicator of the kth network node, r kj is the standard value of the jth indicator of the kth network node.

[0209] In this embodiment, typical scenarios of virtual power plants include typical scenarios of virtual power plants during peak periods, typical scenarios during normal periods, and typical scenarios during valley periods. Different typical scenarios of virtual power plants correspond to different quantitative values ​​of adjustable potential.

[0210] In this embodiment, the second calculation module is specifically used for:

[0211] Based on prospect theory, obtain the benefit decision weight and risk decision weight of each indicator;

[0212] Based on each network node of the virtual power plant, the benefit decision weight of each indicator of the network node is weighted to the positive prospect value of each indicator, and the risk decision weight of each indicator of the network node is weighted to the negative prospect value of each indicator. The weighted results are summed to obtain the adjustable potential quantitative value of the network node at the set time scale; the adjustable potential quantitative value of all network nodes at the set time scale is the adjustable potential quantitative value of the virtual power plant at the set time scale.

[0213] In this embodiment, the adjustable potential indicator set under the set time scale includes one or more of the following:

[0214] Maintenance load index, maintenance rate index, maintenance cost index, unit electricity output value index, unit electricity tax index and unit electricity carbon emission index on a monthly time scale;

[0215] Weekly rest load index, load reduction rate index, rotation cost index, unit electricity output value index, unit electricity tax index and unit electricity carbon emission index on a weekly time scale;

[0216] The staggered load indicators, staggered electricity consumption proportion indicators, staggered cost indicators, unit electricity output value indicators, unit electricity tax indicators and unit electricity carbon emission indicators on a daily time scale.

[0217] Example 3

[0218] like Figure 5 As shown, the present invention also provides an electronic device, which may be a computer device, a single-chip device, an intelligent mobile device, etc. The electronic device in this embodiment may include a processor, a memory, a transceiver component, etc. The memory, the processor, and the transceiver component are connected via a bus; the memory may be used to store an execution program, and an exemplary execution program may include instructions; the processor is used to execute the instructions stored in the memory. The memory may also be used to store data, which may be called and / or modified when the instructions are executed.

[0219] The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, which is suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions in a storage medium to implement corresponding method flows or corresponding functions, so as to implement the steps of a method for quantitatively estimating the adjustable potential of a virtual power plant considering carbon flow in the above-mentioned embodiment.

[0220] Example 4

[0221] Based on the same inventive concept, the present invention also provides a readable storage medium, specifically an electronic device readable storage medium (Memory), which is a memory device in an electronic device for storing programs and data. It can be understood that the storage medium here can include both built-in storage media in electronic devices and, of course, extended storage media supported by electronic devices. The storage medium provides a storage space, which stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by a processor are also stored in the storage space, and these instructions can be one or more execution programs (including program codes). It should be noted that the storage medium here can be a high-speed RAM memory or a non-volatile memory, such as at least one disk storage. The processor loads and executes one or more instructions stored in the storage medium, which can implement the steps of a method for quantitatively estimating the adjustable potential of a virtual power plant considering carbon flow in the above embodiment.

[0222] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may 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.

[0223] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0224] These computer program instructions may also be stored in a computer readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1A function specified in one or more boxes.

[0225] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0226] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit its protection scope. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that after reading the present invention, those skilled in the art can still make various changes, modifications or equivalent substitutions to the specific implementation methods of the application, but these changes, modifications or equivalent substitutions are all within the protection scope of the claims of the present invention.

Claims

1. A method for quantitatively estimating the adjustable potential of a virtual power plant considering carbon flow, characterized in that: include: Based on a pre-constructed set of adjustable potential indicators of a virtual power plant at a set time scale, the value of each indicator in the adjustable potential indicator set is obtained from the historical operation data of the virtual power plant; wherein the historical operation data includes carbon flow data, the adjustable potential indicator set includes a unit electricity carbon emission indicator, and the value of the unit electricity carbon emission indicator is determined according to the node network loss power sharing amount of the virtual power plant calculated according to the carbon flow data; Based on the grey target-foreground theory, the values ​​of each indicator are subjected to grey target transformation, and the results after grey target transformation are coefficientized and input into the foreground value function to obtain the positive and negative foreground values ​​of each indicator. The positive and negative prospect values ​​of each indicator are weighted and summed to obtain the quantified value of the adjustable potential of the virtual power plant under the set time scale.

2. The method according to claim 1, characterized in that The process of determining the value of the carbon emission index per unit of electricity includes: According to the reverse flow distribution matrix considering the network loss in the carbon flow data, the total branch power of the network branch of the virtual power plant is obtained as the total network loss of the virtual power plant; Based on the adjustable network loss allocation coefficient, the total network loss of the virtual power plant is allocated to each network node of the virtual power plant to obtain the node network loss power allocation amount of each network node; Based on each of the network nodes, the node carbon emissions of the network node are divided by the sum of the injected power of the network node and the node network loss power share to obtain the node carbon footprint intensity of the network node as the value of the carbon emission index per unit electricity.

3. The method according to claim 2, characterized in that The method of obtaining the total branch power of the network branches of the virtual power plant as the total network loss of the virtual power plant according to the reverse flow distribution matrix considering the network loss in the carbon flow data includes: The network branches of the virtual power plant are used as virtual nodes, and together with the various network nodes, as a master node; based on the carbon flow data, a reverse flow distribution matrix of the master node considering network loss and a master node injection power matrix are obtained; The inverse matrix of the countercurrent distribution matrix is ​​multiplied by the total node injection power matrix, and the elements corresponding to the virtual nodes in the resultant matrix are added to obtain the total branch power of the network branches of the virtual power plant as the total network loss of the virtual power plant.

4. The method according to claim 2, characterized in that The process of obtaining the node network loss power sharing amount satisfies the following expression: in, is the node network loss power sharing amount of network node k, k = 1,...,n, n is the total number of network nodes, is the injected power of network node k, P 总网损 is the total network loss of the virtual power plant, and β is the adjustable network loss allocation coefficient.

5. The method according to claim 2, characterized in that The process of obtaining the node carbon trace intensity satisfies the following expression: Among them, F k is the node carbon trace intensity of network node k, is the node carbon emission of network node k, is the node network loss power allocation of network node k, is the injected power of network node k.

6. The method according to claim 1, characterized in that Based on the grey target-foreground theory, the values ​​of each indicator are subjected to grey target transformation, and the results after grey target transformation are coefficientized and input into the foreground value function to obtain the positive and negative foreground values ​​of each indicator, including: In a typical scenario of a virtual power plant, an initial evaluation matrix is ​​constructed based on the values ​​of each indicator in the adjustable potential indicator set, wherein a matrix element in the initial evaluation matrix corresponds to the value of a certain indicator of a certain network node of the virtual power plant; According to the positive and negative correlation between each indicator and the adjustable potential of the virtual power plant, a standard sequence of the initial evaluation matrix is ​​obtained, wherein each sequence element in the standard sequence corresponds to a standardized result of the value of each indicator; Based on each of the matrix elements, the matrix element and the sequence element corresponding to its index are input into a grey-target transformation formula to obtain a standard value of the matrix element; Inputting the standard value of the matrix element into the bull's eye coefficient calculation formula to obtain the positive and negative bull's eye coefficients of the matrix element; The positive and negative bull's-eye coefficients of the matrix elements are input into the prospect value function to obtain the positive and negative prospect values ​​of the matrix elements, that is, the positive and negative prospect values ​​of the indicators of the network nodes corresponding to the matrix elements.

7. The method according to claim 6, characterized in that The grey target transformation formula is expressed as: Where T(·) represents the grey target transformation operation formula, d kj is the value of the jth index of the kth network node in the initial evaluation matrix, r kj is the standard value of the jth indicator of the kth network node; is the positive bull's-eye normalized result of the value of the jth indicator. When the jth indicator is positively correlated with the adjustable potential of the virtual power plant, When the jth indicator is negatively correlated with the adjustable potential of the virtual power plant, 8. The method according to claim 6, characterized in that The bull's eye coefficient calculation formula is expressed as: in, is the positive bullseye coefficient of the value of the jth indicator of the kth network node, is the negative bull's eye coefficient of the value of the jth index of the kth network node, ρ is the resolution coefficient, is the grey correlation difference between the positive bull's eye and the standard value of the jth index of the kth network node, is the grey correlation difference between the negative bull's eye and the standard value of the jth indicator of the kth network node, r kj is the standard value of the jth indicator of the kth network node.

9. The method according to claim 6, characterized in that The typical scenarios of the virtual power plant include typical scenarios during peak periods, typical scenarios during normal periods, and typical scenarios during valley periods. Different typical scenarios of virtual power plants correspond to different quantitative values ​​of adjustable potential.

10. The method according to claim 1, characterized in that The weighted summation of the positive and negative prospect values ​​of each indicator to obtain the adjustable potential quantitative value of the virtual power plant at the set time scale includes: Based on prospect theory, obtain the benefit decision weight and risk decision weight of each indicator; Based on each network node of the virtual power plant, the benefit decision weight of each indicator of the network node is weighted to the positive prospect value of each indicator, and the risk decision weight of each indicator of the network node is weighted to the negative prospect value of each indicator. The weighted results are summed to obtain the adjustable potential quantification value of the network node at the set time scale; the adjustable potential quantification value of all network nodes at the set time scale is the adjustable potential quantification value of the virtual power plant at the set time scale.

11. The method according to claim 1, characterized in that The adjustable potential indicator set under the set time scale includes one or more of the following: Maintenance load index, maintenance rate index, maintenance cost index, unit electricity output value index, unit electricity tax index and unit electricity carbon emission index on a monthly time scale; Weekly rest load index, load reduction rate index, rotation cost index, unit electricity output value index, unit electricity tax index and unit electricity carbon emission index on a weekly time scale; The staggered load indicators, staggered electricity consumption proportion indicators, staggered cost indicators, unit electricity output value indicators, unit electricity tax indicators and unit electricity carbon emission indicators on a daily time scale.

12. A virtual power plant adjustable potential quantitative estimation system considering carbon flow, characterized in that: include: An indicator value acquisition module, for obtaining the value of each indicator in the adjustable potential indicator set from the historical operation data of the virtual power plant based on the pre-constructed adjustable potential indicator set of the virtual power plant at a set time scale; wherein the historical operation data includes carbon flow data, the adjustable potential indicator set includes a unit electricity carbon emission indicator, and the value of the unit electricity carbon emission indicator is determined according to the node network loss power sharing amount of the virtual power plant calculated according to the carbon flow data; The first calculation module is used to perform gray target transformation on the value of each indicator based on the gray target-foreground theory, and input the result after the gray target transformation into the foreground value function after coefficientization to obtain the positive and negative foreground value of each indicator; The second calculation module is used to weight the positive and negative prospect values ​​of each indicator to obtain a quantitative value of the adjustable potential of the virtual power plant under the set time scale.

13. The system of claim 12, wherein: The indicator value acquisition module includes: The first submodule is used to obtain the total branch power of the network branch of the virtual power plant as the total network loss of the virtual power plant according to the reverse flow distribution matrix considering the network loss in the carbon flow data; The second submodule is used to allocate the total network loss of the virtual power plant to each network node of the virtual power plant based on the adjustable network loss allocation coefficient, and obtain the node network loss power allocation amount of each network node; The third submodule is used to divide the node carbon emissions of the network node by the sum of the injected power of the network node and the node network loss power share based on each of the network nodes, to obtain the node carbon footprint intensity of the network node as the value of the carbon emission index per unit electricity.

14. The system of claim 13, wherein: The first submodule is specifically used for: The network branches of the virtual power plant are used as virtual nodes, and together with the various network nodes, as a master node; based on the carbon flow data, a reverse flow distribution matrix of the master node considering network loss and a master node injection power matrix are obtained; The inverse matrix of the countercurrent distribution matrix is ​​multiplied by the total node injection power matrix, and the elements corresponding to the virtual nodes in the resultant matrix are added to obtain the total branch power of the network branches of the virtual power plant as the total network loss of the virtual power plant.

15. The system of claim 13, wherein: The process of obtaining the node network loss power sharing amount satisfies the following expression: in, is the node network loss power sharing amount of network node k, k = 1,...,n, n is the total number of network nodes, is the injected power of network node k, P 总网损 is the total network loss of the virtual power plant, and β is the adjustable network loss allocation coefficient.

16. The system of claim 13, wherein: The process of obtaining the node carbon trace intensity satisfies the following expression: Among them, F k is the node carbon trace intensity of network node k, is the node carbon emission of network node k, is the node network loss power allocation of network node k, is the injected power of network node k.

17. The system of claim 12, wherein: The first calculation module is specifically used for: In a typical scenario of a virtual power plant, an initial evaluation matrix is ​​constructed based on the values ​​of each indicator in the adjustable potential indicator set, wherein a matrix element in the initial evaluation matrix corresponds to the value of a certain indicator of a certain network node of the virtual power plant; According to the positive and negative correlation between each indicator and the adjustable potential of the virtual power plant, a standard sequence of the initial evaluation matrix is ​​obtained, wherein each sequence element in the standard sequence corresponds to a standardized result of the value of each indicator; Based on each of the matrix elements, the matrix element and the sequence element corresponding to its index are input into a grey-target transformation formula to obtain a standard value of the matrix element; Inputting the standard value of the matrix element into the bull's eye coefficient calculation formula to obtain the positive and negative bull's eye coefficients of the matrix element; The positive and negative bull's-eye coefficients of the matrix elements are input into the prospect value function to obtain the positive and negative prospect values ​​of the matrix elements, that is, the positive and negative prospect values ​​of the indicators of the network nodes corresponding to the matrix elements.

18. The system of claim 17, wherein: The grey target transformation formula is expressed as: Where T(·) represents the grey target transformation operation formula, d kj is the value of the jth index of the kth network node in the initial evaluation matrix, r kj is the standard value of the jth indicator of the kth network node; is the positive bull's-eye normalized result of the value of the jth indicator. When the jth indicator is positively correlated with the adjustable potential of the virtual power plant, When the jth indicator is negatively correlated with the adjustable potential of the virtual power plant, 19. The system of claim 17, wherein: The bull's eye coefficient calculation formula is expressed as: in, is the positive bullseye coefficient of the value of the jth indicator of the kth network node, is the negative bull's eye coefficient of the value of the jth index of the kth network node, ρ is the resolution coefficient, is the grey correlation difference between the positive bull's eye and the standard value of the jth index of the kth network node, is the grey correlation difference between the negative bull's eye and the standard value of the jth indicator of the kth network node, r kj is the standard value of the jth indicator of the kth network node.

20. The system of claim 17, wherein: The typical scenarios of the virtual power plant include typical scenarios during peak periods, typical scenarios during normal periods, and typical scenarios during valley periods. Different typical scenarios of virtual power plants correspond to different quantitative values ​​of adjustable potential.

21. The system of claim 12, wherein: The second calculation module is specifically used for: Based on prospect theory, obtain the benefit decision weight and risk decision weight of each indicator; Based on each network node of the virtual power plant, the benefit decision weight of each indicator of the network node is weighted to the positive prospect value of each indicator, and the risk decision weight of each indicator of the network node is weighted to the negative prospect value of each indicator, and the weighted results are summed to obtain the adjustable potential quantified value of the network node under the set time scale; The quantified value of the adjustable potential of all network nodes at the set time scale is the quantified value of the adjustable potential of the virtual power plant at the set time scale.

22. The system of claim 12, wherein: The adjustable potential indicator set under the set time scale includes one or more of the following: Maintenance load index, maintenance rate index, maintenance cost index, unit electricity output value index, unit electricity tax index and unit electricity carbon emission index on a monthly time scale; Weekly rest load index, load reduction rate index, rotation cost index, unit electricity output value index, unit electricity tax index and unit electricity carbon emission index on a weekly time scale; The staggered load indicators, staggered electricity consumption proportion indicators, staggered cost indicators, unit electricity output value indicators, unit electricity tax indicators and unit electricity carbon emission indicators on a daily time scale.

23. An electronic device, characterized in that: include: at least one processor and memory; The memory and the processor are connected via a bus; The memory is used to store one or more programs; When the one or more programs are executed by the at least one processor, a method for quantitatively estimating the adjustable potential of a virtual power plant considering carbon flow as described in any one of claims 1 to 11 is implemented.

24. A readable storage medium, characterized in that: An execution program is stored thereon, and when the execution program is executed, a method for quantitatively estimating the adjustable potential of a virtual power plant considering carbon flow as described in any one of claims 1 to 11 is implemented.

Images