A multi-microgrid collaborative operation strategy based on Nash negotiation

Abstract

The present application relates to the technical field of multi-microgrid coordinated optimization operation, and specifically to a multi-microgrid coordinated operation strategy based on Nash negotiation. The strategy includes: obtaining the load-side power consumption and active power of each microgrid at each moment, the microgrid frequency and power factor on the power supply side, and the renewable energy generation; calculating a first stability and a first anomaly to obtain the absorption assessment value of each microgrid; calculating a second stability and a second anomaly to obtain the response imbalance of each microgrid; determining the contribution factor of each microgrid; constructing a Nash negotiation model for the coordinated operation cost of multiple microgrids, establishing an objective function based on minimizing the operating cost of the microgrid; solving the model using the alternating direction multiplier method, and combining the contribution factors to allocate the costs of the microgrids using the Shapley value method. The present application can improve the irrational distribution of benefits caused by ignoring differences in source-load characteristics in traditional Nash negotiation, and improve the stability and sustainability of the coordinated operation of multiple microgrids.

Description

Technical Field

[0001] The present application relates to the technical field of multi-microgrid coordinated optimization operation, and specifically to a multi-microgrid coordinated operation strategy based on Nash negotiation. Background Art

[0002] With the further development of distributed energy technologies, low-carbon energy operations, and modern power systems, multi-microgrid systems and renewable energy generation have been widely applied and developed. The shift from isolated single-microgrid operation to more complex multi-microgrid source-load complementary operation has become a key focus of power transformation research. The coordinated operation of multiple microgrids can achieve complementary advantages and energy flow between various renewable energy sources. Nash negotiation, a core model in cooperative game theory, aims to determine fair resource allocation solutions among multiple participants in negotiations through mathematical axiomatic methods. It can effectively address the challenges posed by the intermittent and uncertain nature of distributed energy resources in multi-microgrid systems, enhance the flexibility and adaptability of power systems, promote the absorption of renewable energy, and ensure the safe and stable operation of the entire power system and the balance of supply and demand.

[0003] The traditional multi-microgrid collaborative optimization strategy based on Nash negotiation does not take into account the differences in source-load characteristics between different microgrids, resulting in limited collaborative scheduling capabilities for the absorption of new energy power. Moreover, when Nash negotiation lacks consideration of the different load characteristics of non-homogeneous microgrids, it may also cause an imbalance in the load demand response of the power system, seriously affecting the complementary advantages and fault support capabilities during the collaborative operation of multiple microgrids, aggravating the risk of unstable operation of the power system, and reducing the efficiency and reliability of the collaborative operation of multiple microgrids. Summary of the Invention

[0004] In order to solve the above technical problems, a multi-microgrid collaborative operation strategy based on Nash negotiation is provided to solve the existing problems.

[0005] The solution to the technical problem in this application is to provide a multi-microgrid collaborative operation strategy based on Nash negotiation, including the following steps:

[0006] Analyze the discrete changes in the active power and renewable energy generation on the load side of each microgrid when they are not zero, as well as the discrete situations at the corresponding moments when they are not zero, calculate the first stability, and combine the differences in the extreme changes in load-side power consumption in different time periods and the periodic characteristics of load-side power consumption to obtain the absorption assessment value of each microgrid;

[0007] The recovery rate is calculated by analyzing the rate of change of the microgrid frequency on the power supply side of each microgrid from the extreme point to the rated frequency. The relevant conditions of the recovery rate and the difference between the power consumption on the load side and the power generation of renewable energy are analyzed to calculate the second stability. The response imbalance degree of each microgrid is obtained by combining the difference in the extreme changes of the power factor on the power supply side between adjacent time periods and the difference in the average level of the power factor. Combined with the absorption evaluation value, the contribution factor of each microgrid is determined; a Nash negotiation model for the collaborative operation cost of multiple microgrids is constructed, and the objective function is established by minimizing the operation cost of the microgrid; the alternating direction multiplier method is used for iterative solution, and combined with the contribution factor, the cost of the microgrid is allocated through the Shapley value method.

[0008] Preferably, the calculating the first stability includes:

[0009] The moment when the renewable energy power generation in each microgrid is not 0 is recorded as the output moment;

[0010] Calculate the discrete degree of all output moments of each microgrid, which is recorded as time dispersion;

[0011] Calculate the sum of the discrete degree of active power on the load side of each microgrid at all times and the discrete degree of renewable energy power generation corresponding to all output times;

[0012] The first stability is a ratio of the time dispersion to the sum value.

[0013] Preferably, obtaining the consumption evaluation value of each microgrid includes:

[0014] Decompose the load-side power consumption trend at all times within each period and calculate the periodic intensity;

[0015] Calculate the range of power consumption on the load side at all times in each period, and record it as the first range;

[0016] fusing the first extreme difference and the periodic intensity of each microgrid in all time periods to calculate a first abnormality degree of each microgrid;

[0017] The absorption evaluation value is a normalized result of the ratio of the first abnormality degree to the first stability degree.

[0018] Preferably, the first abnormality degree is the product of the mean of the first range in all time periods of each microgrid and the mean of the periodicity intensity in all time periods.

[0019] Preferably, the recovery rate is calculated as follows:

[0020] Obtain the extreme points of the microgrid frequency at all times under each microgrid, including the maximum and minimum points; record the difference between the extreme value corresponding to each extreme point and the rated frequency as the relative deviation;

[0021] Calculate the time interval between the corresponding moment of each extreme point and the corresponding moment when the microgrid frequency gradually decreases to the rated frequency;

[0022] The ratio of the relative deviation to the time interval is used as the recovery rate of each extreme point.

[0023] Preferably, the calculating the second stability comprises:

[0024] Calculating the autocorrelation coefficients of the recovery rates of all maximum points of each microgrid; calculating the autocorrelation coefficients of the recovery rates of all minimum points of each microgrid;

[0025] The sum of all autocorrelation coefficients corresponding to the maximum point and the autocorrelation coefficient corresponding to the minimum point under each microgrid is taken as the correlation degree of each microgrid;

[0026] Calculate the cumulative sum of the differences between the load-side power consumption and renewable energy generation of each microgrid at each moment;

[0027] The second stability is a ratio of the correlation to the cumulative sum.

[0028] Preferably, obtaining the response imbalance degree of each microgrid includes:

[0029] Calculate the range of the power factor on the power supply side of each microgrid at all times in each time period, and record it as the second range difference; record the sum of the differences of the second range differences between all two adjacent time periods in each microgrid as the relative difference;

[0030] Calculate the average power factor of each microgrid at all times in each time period on the power supply side; record the sum of the differences between the average values ​​of all two adjacent time periods in each microgrid as the average difference;

[0031] fusing the average difference and the relative difference to calculate a second abnormality degree of each microgrid;

[0032] The response imbalance degree is a normalized result of a ratio of the second abnormality degree to the second stability degree.

[0033] Preferably, the second abnormality degree is the sum of a result of positive mapping of the average difference and a result of positive mapping of the relative difference.

[0034] Preferably, the process of obtaining the contribution factor is: comprehensively evaluating the response imbalance and the absorption evaluation value of all microgrids, and calculating the comprehensive score of each microgrid; the contribution factor is the comprehensive score.

[0035] Preferably, the construction of a Nash negotiation model for the collaborative operation cost of multiple microgrids includes: ,in, For the The operating cost of each microgrid when operating independently is the point at which negotiations break down; To participate in the collaborative operation The operating cost of a microgrid, is the number of all microgrids, is the maximum value function.

[0036] Preferably, the objective function is: ,in, For the The operating cost of a microgrid, 、 、 、 Respectively The operating costs of gas turbines, fuel cells, photovoltaic power generation, and wind turbine power generation equipment in a microgrid are: For the The power cost of interaction between a microgrid and the large grid, For the The total interaction cost between a microgrid and all other microgrids, For the The carbon trading cost of a microgrid, For the The operating cost of a microgrid and energy storage system for power cooperation, Indicates the Minimize the operating cost of a microgrid.

[0037] Preferably, the cost allocation of the microgrid using the Shapley value method includes:

[0038] All microgrids are grouped into a set N, and all non-empty subsets of the set N are obtained, including the All subsets of microgrids constitute the set , will be collected Any element in is denoted as alliance S;

[0039] The corrected Shapley value of the i-th microgrid for: ,in, is the operating cost of the qth microgrid in the alliance S when it operates independently, is the total operating cost of all microgrids in alliance S, To save costs for Alliance S, Remove the first for Alliance S Cost savings of a microgrid, For the League S Cost savings of a microgrid, For the League S Cost savings after microgrid adjustment, is the number of all microgrids in the set N, is the number of microgrids in the alliance S, For the The contribution factor of a microgrid is represents the factorial symbol, Indicates belonging to the collection.

[0040] This application has at least the following beneficial effects:

[0041] This application calculates the first stability, which has the beneficial effect of reflecting the rigidity of the microgrid's absorption of new energy power through the fluctuation between the active power on the load side and the new energy power generation, as well as the concentration of the new energy power in each microgrid to the load side response output time, and explains the unstable operation of the microgrid; secondly, the application calculates the first abnormality, which has the beneficial effect of taking into account the rigid periodicity of the power consumption on the load side and the difference between the peak and valley of the load, so as to evaluate the abnormality of the load characteristics in the microgrid, and then obtain the absorption evaluation value of each microgrid, which has the beneficial effect of taking into account the The grid's rigidity in absorbing and dispatching new energy power reflects the microgrid's utilization efficiency and absorption potential for new energy power; the second stability is calculated, which has the beneficial effect of considering the recovery and adjustment ability of each microgrid after the microgrid frequency deviation on the power supply side, as well as the balance between new energy power and load demand, to preliminarily reflect the significance of the load demand response imbalance in the microgrid; the second abnormality is calculated, which has the beneficial effect of considering the significance of the power factor sudden drop trend, further reflecting the impact of the microgrid on the load demand response imbalance; the response imbalance of each microgrid is obtained. The beneficial effect is that the frequency and power factor imbalance of the microgrid on the power supply side are used to comprehensively evaluate the significance of the load demand response imbalance of the microgrid; the contribution factor of each microgrid is determined, which is beneficial in that the contribution of the microgrid to the stability of the overall coordinated operation is considered when the microgrid has a low level of rigidity in absorbing and dispatching new energy power and the load demand response imbalance caused by the source-load characteristic difference is relatively mild, so as to obtain more favorable cooperative benefit distribution conditions in the subsequent Nash negotiation; a Nash negotiation model for the coordinated operation cost of multiple microgrids is constructed, and the operation of the microgrid is used as the basis for the optimization of the cost of the coordinated operation of the microgrid. The objective function is established by minimizing the operation cost; the alternating direction multiplier method is used to iteratively solve the model, and the contribution factor is combined to allocate the cost of the microgrid through the Shapley value method. Its beneficial effect is that it can fully tap the new energy power absorption capacity and load demand response capacity in the coordinated operation of multiple microgrids, improve the irrational benefit distribution caused by ignoring the source-load characteristic differences in traditional Nash negotiation, effectively improve the stability and sustainability of the coordinated operation of multiple microgrids, promote long-term cooperation and common development among multiple microgrids, and ensure the stable operation and supply and demand balance of the entire power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] The following is a further detailed description of a multi-microgrid collaborative operation strategy based on Nash negotiation of the present application with reference to the accompanying drawings.

[0043] Figure 1 A flowchart of the steps of a multi-microgrid collaborative operation strategy based on Nash negotiation provided in an embodiment of the present application;

[0044] Figure 2 A schematic diagram of a multi-microgrid system architecture provided in an embodiment of the present application;

[0045] Figure 3 Flowchart of the method for obtaining the contribution factor provided in an embodiment of the present application. DETAILED DESCRIPTION

[0046] To make the objectives, technical solutions, and advantages of this application more clearly understood, the following, in conjunction with the accompanying drawings and implementation examples, further details are provided on a multi-microgrid collaborative operation strategy based on Nash negotiation proposed in this application. It should be understood that the specific embodiments described herein are merely intended to explain this application and are not intended to limit this application.

[0047] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

[0048] See also Figure 1 , which shows a flowchart of a multi-microgrid collaborative operation strategy based on Nash negotiation provided by an embodiment of the present application. The strategy includes the following steps:

[0049] Step 1: Obtain the power consumption and active power of each microgrid on the load side, the frequency and power factor of the microgrid on the power supply side, and the renewable energy power generation at each moment; divide all moments into multiple time periods.

[0050] Against the backdrop of increasingly severe energy and environmental challenges, developing renewable energy and promoting efficient, cascaded energy utilization have become important reform directions in the energy sector. In multi-microgrid systems, integrating multiple microgrids enables diversified energy utilization and comprehensive optimization. Multiple independent microgrids form alliances through energy interaction to improve overall energy efficiency and economic benefits. However, due to differences in individual microgrids' energy structures, load characteristics, and interests, coordinating cooperation and equitably distributing benefits poses a key challenge. Nash negotiation theory, a cooperative game approach, has been widely used in multi-microgrid optimal scheduling due to its unique advantages in addressing multi-party interest allocation.

[0051] The schematic diagram of the multi-microgrid system architecture provided in this embodiment is as follows Figure 2As shown in the figure, a single microgrid includes photovoltaic generation (PV), wind turbine generation (WT), gas turbine units (GT), fuel cells (FC), electric energy storage (EES) and load. Microgrid 1 does not contain a battery. The single microgrid and the large grid are connected to the common bus through a transformer. The microgrids are connected in a hybrid AC / DC mode, and energy exchange is achieved through AC / DC or DC / DC. When the load of a microgrid is overloaded, the other microgrids give priority to transmitting electricity through the converter, and then purchase electricity from the large grid.

[0052] In a multi-microgrid system, due to the differences in energy structure, load characteristics, and interest demands of different microgrids, the degree of contribution of each microgrid to the coordinated operation of the multi-microgrid system is different. Therefore, the final distribution of benefits should be in line with the contribution of each microgrid.

[0053] Smart meters, synchronized phasor measurement units (PMUs), power factor meters, and automatic generation control systems (AGCs) are deployed in a multi-microgrid system. The smart meters are used to obtain the power consumption and active power on the load side of each microgrid in real time. The automatic generation control system monitors the renewable energy power generation of each microgrid in real time, and synchronized phasor measurement units and power factor meters are used to collect the microgrid frequency and power factor on the power supply side of each microgrid in real time. The data collected at all times of the day are normalized, missing values ​​are filled, and filtered to obtain the power consumption and active power on the load side, the microgrid frequency and power factor on the power supply side, and the renewable energy power generation of each microgrid at each moment.

[0054] In this embodiment, Z-Score standardization is used for normalization processing, mean filling method is used for missing value filling, and wavelet transform algorithm is used for filtering and denoising processing. Among them, Z-Score standardization, mean filling method and wavelet transform algorithm are all well-known technologies and will not be described in detail here.

[0055] At this point, the power consumption and active power of each microgrid on the load side at each moment, the microgrid frequency and power factor on the power supply side, and the renewable energy power generation are obtained.

[0056] Step 2: Analyze the discrete changes in the active power and renewable energy power generation of each microgrid when they are not zero, as well as the discrete situations at the corresponding moments when they are not zero, and calculate the first stability. Calculate the first abnormality by taking into account the differences in the extreme changes in power consumption in different time periods and the periodic characteristics of power consumption. Combined with the first stability, the absorption assessment value of each microgrid is obtained.

[0057] In the process of grid-connected coordinated operation of multiple microgrids, the rigidity of the new energy power consumption level is of great significance to the Nash negotiation of multi-microgrid coordinated operation. The rigidity of new energy consumption scheduling reflects the degree of rigidity of microgrids in the consumption and scheduling of new energy power in grid-connected coordinated operation. The higher the rigidity level, the lower the efficiency of microgrids in utilizing new energy power. This will not only increase the overall power generation cost, but also cause the microgrid to be unable to fully utilize the potential of new energy consumption, thereby limiting the flexible adjustment of microgrid coordination strategies such as optimization scheduling space and demand response in the process of coordinated operation, and ultimately affecting the fairness of interest distribution in the cooperation of multi-microgrid systems.

[0058] The serious rigidity of renewable energy consumption and dispatch is primarily reflected in abnormal load fluctuations caused by the influx of renewable energy into the grid, as well as abnormal active power conditions caused by renewable energy inflows into microgrids. During the coordinated operation of microgrids, when renewable energy generation does not match load demand and scheduling is rigid, the microgrid load lacks sufficient flexibility for adjustment, exhibiting a strong rigid periodicity. This reduces the ability to smooth out peaks and fill valleys, and the peak-to-valley difference in load remains high. Furthermore, a high proportion of renewable energy consumption and rigid dispatching exacerbate active power fluctuations on the load side, making the output probability distribution of renewable energy power more concentrated in response to the load side.

[0059] Based on the above analysis, by analyzing the matching situation between the active power on the load side and the renewable energy generation, the absorption assessment value is calculated to reflect the level of rigidity of the microgrid's absorption and dispatching of renewable energy power in the grid-connected coordinated operation.

[0060] First, analyze the periodic changes and extreme changes in power consumption on the load side of each microgrid and calculate the first abnormality degree, specifically:

[0061] Divide all moments into multiple time periods;

[0062] In this embodiment, all moments are evenly divided into 12 time periods. As other implementation methods, implementers can set them according to actual conditions.

[0063] Decompose the load-side power consumption trend at all times within each period and calculate the periodic intensity;

[0064] In this embodiment, the STL trend decomposition algorithm (Seasonal and Trend decomposition using Loess) is used for trend decomposition. The STL trend decomposition algorithm and the calculation of the periodic term strength are both well-known technologies and will not be described in detail here. The power consumption of the load side at all times in each period is decomposed into the residual term , Seasonal items , then the calculation formula for the periodic term strength is: ,in, is the periodic intensity, is the variance of the residual term, is the variance of the residual term and seasonal term, To find the maximum value.

[0065] Calculate the range of power consumption on the load side at all times in each period, and record it as the first range;

[0066] The product of the mean of the first range difference of each microgrid in all time periods and the mean of the periodic intensity of all time periods is used as the first abnormality degree of each microgrid;

[0067] It should be noted that the first abnormality degree reflects the rigid periodicity and load peak-valley difference of the load data of the microgrid. The larger the first abnormality degree is, the more unstable the load characteristics in the microgrid are.

[0068] Secondly, by analyzing the fluctuation of renewable energy power generation, the fluctuation of active power on the load side, and the concentration of renewable energy output, the first stability is calculated, specifically:

[0069] The moment when the renewable energy power generation in each microgrid is not 0 is recorded as the output moment;

[0070] Calculate the discrete degree of all output moments of each microgrid, which is recorded as time dispersion;

[0071] In this embodiment, the degree of dispersion is measured by calculating the variance of all output moments of each microgrid. As other implementation methods, implementers can adopt other methods of the prior art, such as standard deviation, coefficient of variation, etc. This embodiment does not impose any special restrictions on this.

[0072] It should be noted that the greater the time dispersion, the greater the difference in renewable energy power generation at different times and the more unstable the output.

[0073] Calculate the sum of the discrete degree of active power on the load side at all times under each microgrid and the discrete degree of renewable energy power generation corresponding to all output times;

[0074] In this embodiment, the degree of dispersion is measured by calculating the variance of the active power at all times under each microgrid, and the variance of the renewable energy power generation corresponding to all output times. As other implementation methods, the implementer can adopt other methods of the existing technology, such as standard deviation, coefficient of variation, etc., and this embodiment does not impose any special restrictions on this.

[0075] The ratio of the time dispersion to the sum is used as the first stability of each microgrid;

[0076] It should be noted that the sum value reflects the fluctuation of the active power of renewable energy power generation and the load side. The larger the sum value is, the more unstable the operation of the microgrid is. The smaller the time dispersion is, the more concentrated the output time of renewable energy is, and the lower the flexibility of regulating power consumption is. The smaller the first stability is, the more significant the fluctuation of active power in the microgrid is, and the more unstable the operation of the microgrid is.

[0077] Furthermore, based on the first abnormality and the first stability, a consumption evaluation value is determined, specifically:

[0078] Normalizing the ratio of the first abnormality degree to the first stability degree as the absorption evaluation value of each microgrid;

[0079] In this embodiment, the sigmoid function is used for normalization processing, wherein the sigmoid function is a well-known technology and will not be described in detail here. As other implementation methods, the implementer can adopt other methods of the existing technology, such as the softmax activation function, etc. This embodiment does not impose any special restrictions on this.

[0080] It should be noted that the greater the first abnormality, the more rigid the periodic changes in the load of the microgrid and the difference in load peaks and valleys, which reflects the load fluctuation of the microgrid, indicating that the higher the rigidity of the microgrid in accommodating and dispatching new energy power. The smaller the first stability, the more significant the fluctuation of the active power of the microgrid, and the larger the obtained absorption evaluation value, indicating that the higher the rigidity of the microgrid in accommodating and dispatching new energy power.

[0081] At this point, the consumption evaluation value of each microgrid is obtained.

[0082] Step 3: Calculate the recovery rate by analyzing the rate of change of the microgrid frequency at all times from the extreme point to the rated frequency. Analyze the relevant conditions of the recovery rate and the difference between power consumption and renewable energy power generation to calculate the second stability. Calculate the second abnormality based on the difference in extreme changes in the power factor between adjacent time periods and the difference in the average level of the power factor. Combined with the second stability, the response imbalance degree of each microgrid is obtained.

[0083] When conducting Nash negotiation on all microgrids in a multi-microgrid system, there are certain drawbacks in evaluating the contribution of microgrids only by the rigidity of their consumption and scheduling of new energy power. This does not take into account the imbalance in the load demand response of different microgrids. As a result, although the microgrid has a strong ability to consume new energy, it has an imbalance in its load demand response. The imbalance in the frequency and power factor of the microgrid on the power supply side will be aggravated, making it unable to effectively participate in the regulation of power load. The contribution to the overall coordinated operation will also be greatly restricted, resulting in unreasonable contribution distribution of each microgrid in the coordinated operation of the overall multi-microgrid system.

[0084] Secondly, during the coordinated operation of multiple microgrid systems, load imbalance will lead to obvious power supply and demand mismatch, delayed and unstable microgrid frequency recovery, and difficulty in balancing renewable energy power generation and load demand; at the same time, the imbalance in load demand response can easily cause three-phase imbalance; in addition, the imbalance in load demand response makes the fluctuation range of power factor significantly expand, resulting in a sudden change in reactive power demand, and the compression of reactive power regulation margin by active power demand will cause a prominent drop in power factor.

[0085] Based on the above analysis, by analyzing the changes in the microgrid frequency and power factor on the power supply side, the response imbalance is calculated to evaluate the reliability and timeliness of the microgrid's response to load demand.

[0086] First, by analyzing the changes in the microgrid frequency on the power supply side and the difference between the power consumption on the load side and the power generation of renewable energy, the second stability is calculated. Specifically,

[0087] Obtain the extreme points of the microgrid frequency at all times under each microgrid;

[0088] In this embodiment, an AMPD (Automatic Multiscale-based Peak Detection) peak detection algorithm is used to obtain extreme value points, including maximum value points and minimum value points. The AMPD algorithm is a well-known technology and will not be described in detail here.

[0089] The difference between the extreme value corresponding to each extreme point of each microgrid and the rated frequency is recorded as the relative deviation;

[0090] In this embodiment, the rated frequency refers to the target frequency value of the AC power under normal operating conditions of each microgrid, which is used to maintain the stable operation of the power grid. The rated frequency of domestic microgrids is 50 Hz. Secondly, the absolute value of the difference between the extreme value corresponding to each extreme point in each microgrid and the rated frequency is recorded as the relative deviation.

[0091] Calculate the time interval between the corresponding moment of each extreme point and the corresponding moment when the microgrid frequency gradually decreases to the rated frequency;

[0092] The ratio of the relative deviation to the time interval is used as the recovery rate of each extreme point;

[0093] Calculating the autocorrelation coefficients of the recovery rates of all maximum points of each microgrid; calculating the autocorrelation coefficients of the recovery rates of all minimum points of each microgrid;

[0094] In this embodiment, when the autocorrelation coefficient is calculated by the autocorrelation function, the lag order is set to 3. As other implementation methods, the implementer can set it according to actual conditions.

[0095] The sum of all autocorrelation coefficients corresponding to the maximum point and the autocorrelation coefficient corresponding to the minimum point under each microgrid is taken as the correlation degree of each microgrid;

[0096] It should be noted that the recovery rate reflects the recovery ability of the microgrid after the frequency deviation. The larger the autocorrelation coefficient, the greater the obtained correlation, which means that the microgrid frequency on the power supply side is affected by the power demand, and the delay in the microgrid adjusting to restore to the rated frequency is lower. The recovery rate of the microgrid after the frequency deviation is consistent and stable, and the load response stability of the microgrid is higher.

[0097] Calculate the cumulative sum of the differences between the load-side power consumption and renewable energy generation at each microgrid at each moment;

[0098] using a ratio of the correlation to the accumulated sum as a second stability of each microgrid;

[0099] It should be noted that the larger the cumulative sum, the greater the difference between the new energy power and the microgrid load, the lower the matching condition between the new energy power and the load demand, the smaller the obtained second stability, and the more significant the imbalance of the load demand response of the microgrid.

[0100] Secondly, analyze the difference in power factor on the power supply side and calculate the second abnormality, specifically:

[0101] Calculate the range of the power factor of each microgrid at all times in each time period on the power supply side, which is recorded as the second range;

[0102] It should be noted that, the larger the second range difference is, the more serious the three-phase imbalance phenomenon caused by the imbalance in load demand response in the microgrid is.

[0103] The sum of the differences of the second range differences between all two adjacent time periods in each microgrid is recorded as the relative difference;

[0104] In this embodiment, the absolute value of the difference between the second extreme difference in each time period and the previous time period in each microgrid is calculated.

[0105] Calculate the average value of the power factor on the power supply side at all times in each period of each microgrid;

[0106] The sum of the differences between the average values ​​of all two adjacent time periods in each microgrid is recorded as the average difference;

[0107] In this embodiment, the absolute value of the difference between the average value in each time period and the next time period in each microgrid is calculated.

[0108] The sum of the result of positive mapping of the average difference and the result of positive mapping of the relative difference is used as the second abnormality degree of each microgrid;

[0109] In this embodiment, the specific process of positive mapping is: positive mapping is performed through an exponential function, assuming that the average difference and the relative difference are respectively recorded as 、 , then The result of the positive mapping of the average difference is taken as the result of the positive mapping of the average difference. The result of the positive mapping of the relative difference is as follows: is an exponential function with a natural constant as its base.

[0110] It should be noted that by performing positive mapping, the significance of the difference in power factor between adjacent time periods is expanded; secondly, the larger the relative difference, the greater the extreme change range of the power factor between adjacent time periods, the larger the average difference, the more significant the sudden drop trend of the power factor between adjacent time periods, and the larger the second abnormality obtained, the more obvious the trend of expanding the fluctuation range of the power factor of the microgrid, and the prominent sudden drop phenomenon of the power factor, reflecting that the microgrid is affected by the imbalance of load demand response.

[0111] Furthermore, based on the second stability and the second abnormality, a response imbalance degree is calculated, specifically:

[0112] Normalizing the ratio of the second abnormality to the second stability as the response imbalance of each microgrid;

[0113] In this embodiment, the sigmoid function is used for normalization processing, wherein the sigmoid function is a well-known technology and will not be described in detail here. As other implementation methods, the implementer can adopt other methods of the existing technology, such as the softmax activation function, etc. This embodiment does not impose any special restrictions on this.

[0114] It should be noted that, the greater the response imbalance, the more obvious the load demand response imbalance of the microgrid.

[0115] At this point, the response imbalance degree of each microgrid is obtained.

[0116] Step 4: Based on the response imbalance and the absorption evaluation value, the contribution factor of each microgrid is determined; a Nash negotiation model for the collaborative operation cost of multiple microgrids is constructed, and the objective function is established by minimizing the operation cost of the microgrid; the alternating direction multiplier method is used to iteratively solve the model, and the cost of the microgrid is allocated using the Shapley value method in combination with the contribution factor.

[0117] The lower the level of rigidity of the microgrid's scheduling for new energy consumption and the milder the imbalance in load demand response, the greater the improvement of the overall interests of the microgrid in the Nash negotiation of the multi-microgrid system. The stronger its bargaining power should be in the Nash negotiation, and the greater its influence on the cooperative stability of the coordinated operation optimization of the multi-microgrid system. In other words, the higher its contribution to the overall interests, the higher its cooperation value, and the more likely it is to obtain more favorable conditions for the distribution of cooperative benefits.

[0118] Therefore, based on the response imbalance and the absorption evaluation value, a contribution factor is determined to characterize the cooperative value and benefit distribution benefits of the microgrid in the process of grid-connected coordinated operation of the multi-microgrid system, specifically:

[0119] Comprehensively evaluating the response imbalance degrees and the absorption evaluation values ​​of all microgrids, calculating a comprehensive score for each microgrid, and using the comprehensive score as a contribution factor for each microgrid;

[0120] In this embodiment, the TOPSIS method (Technique for Order Preference by Similarity to an Ideal Solution) and the entropy weight method are used to calculate the comprehensive score. The TOPSIS method and the entropy weight method are both well-known technologies and will not be described in detail here.

[0121] It should be noted that the higher the comprehensive score, the lower the rigidity of the microgrid's consumption and scheduling of new energy power during the grid-connected coordinated operation, the milder the load demand response imbalance caused by the source-load characteristic difference, the greater the contribution to the overall stability of the coordinated operation of the multi-microgrid system, and the more favorable cooperative benefit distribution conditions should be obtained in the Nash negotiation. The flowchart of the method for obtaining the contribution factor provided in the embodiment of the present application is as follows: Figure 3 shown.

[0122] Nash negotiation, also known as the "bargaining" model, is one of the earliest problems studied in game theory and an important theoretical foundation for cooperative games. The goal of bargaining is to maximize personal gain, but conflicts of interest between participating parties limit the extent of gains. Negotiations break down if these limits are exceeded. Therefore, using the aforementioned contribution factors, the specific steps for a multi-microgrid collaborative operation strategy based on Nash negotiation are as follows:

[0123] A multi-microgrid system is composed of multiple microgrids. The direct electricity trading partners of each microgrid entity can be the main grid and other microgrids. The main operating costs and benefits of the microgrid include the cost of purchasing and selling electricity from the main grid, the operating costs of fuel cells, the interaction costs between microgrids, the photovoltaic operation and maintenance costs, the gas turbine operation costs, the wind turbine maintenance and operation costs, the carbon emission and environmental protection costs, and the interaction costs between the microgrid and the energy storage system.

[0124] The coordinated operation of multiple microgrid systems requires that each microgrid should take the minimum comprehensive energy cost as the optimization goal under the premise of achieving internal power balance in the microgrid. Regardless of whether the electric energy is transmitted between different microgrids or directly interacts with the large power grid, transmission costs and network access costs are required. However, due to the interactive power between microgrids.

[0125] When each microgrid operates independently, the objective function of its operating cost is:

[0126]

[0127] in, For the The operating cost of a microgrid when it operates independently, 、 、 、 Respectively The operating costs of gas turbines, fuel cells, photovoltaic power generation, and wind turbine power generation equipment in a microgrid are: For the The power cost of interaction between a microgrid and the large grid, For the The first microgrid and the The interaction power cost between microgrids, For the The carbon trading cost of a microgrid, For the The operating cost of a microgrid and energy storage system for power cooperation; Indicates the Minimize the operating cost of each microgrid;

[0128] When multiple microgrids operate in coordination, the objective function of the coordinated operation cost of each microgrid is:

[0129]

[0130] in, For the The operating cost of each microgrid when participating in collaborative operation, 、 、 、 Respectively The operating costs of gas turbines, fuel cells, photovoltaic power generation, and wind turbine power generation equipment in a microgrid are: For the The power cost of interaction between a microgrid and the large grid, For the The total interaction cost between a microgrid and all other microgrids, For the The carbon trading cost of a microgrid, For the The operating cost of a microgrid and energy storage system for power cooperation;

[0131] No. Operating costs of a gas turbine unit in a microgrid satisfy:

[0132]

[0133] in, For the The cost coefficient of unit electricity generated by the gas turbine unit in the microgrid is: is the electric power of the gas turbine unit in the i-th microgrid at time t, T is all time, that is, all time in a day, where T is 24 hours;

[0134] No. Operating costs of fuel cells in a microgrid satisfy:

[0135]

[0136] in, For the The cost coefficient of unit electricity generated by fuel cells in a microgrid, is the electric power of the fuel cell in the i-th microgrid at time t;

[0137] No. The operating cost of photovoltaic power generation equipment in a microgrid satisfy:

[0138]

[0139] in, For the The cost coefficient of photovoltaic power generation per unit of electricity in a microgrid, is the electric power of the photovoltaic power generation equipment in the i-th microgrid at time t;

[0140] No. The operating cost of wind turbine generator equipment in a microgrid satisfy:

[0141]

[0142] in, For the The cost coefficient of the wind turbine generating unit electricity in a microgrid, is the electric power of the wind turbine generator in the i-th microgrid at time t;

[0143] No. The power cost of interaction between microgrid and large grid satisfy:

[0144]

[0145] in, is the price at which the i-th microgrid purchases electricity from the large grid at time t, is the price of electricity sold by the i-th microgrid to the large grid at time t, is the amount of electricity purchased by the i-th microgrid from the large grid at time t, is the amount of electricity sold by the i-th microgrid to the main grid at time t;

[0146] No. The first microgrid and the Interaction power cost between microgrids for:

[0147]

[0148] in, is the relationship between the i-th microgrid and the The electricity selling price of a microgrid, The i-th microgrid at time t is The amount of electricity purchased by a microgrid, The tth time is the time when the i-th microgrid sends The amount of electricity sold by each microgrid;

[0149] No. The total interaction cost between a microgrid and all other microgrids satisfy:

[0150]

[0151] in, The first Microgrid to The interaction cost per unit of electricity between microgrids, The first The microgrid transmits the data to the The interactive power of a microgrid, is the number of all microgrids in the multi-microgrid system;

[0152] No. Carbon trading costs for microgrids satisfy:

[0153]

[0154] in, is the cost coefficient of unit CO2 governance, is the CO2 emission coefficient per unit electricity of the gas turbine, is the CO2 emission coefficient per unit power of the large power grid;

[0155] No. The operating cost of a microgrid and energy storage system for power cooperation satisfy:

[0156]

[0157] in, For the The cost coefficient required for a microgrid to trade unit electricity with the energy storage system, The battery in the energy storage system at time t is The discharge power of a microgrid, is the discharge efficiency of the battery, The battery in the energy storage system at time t sends The charging power of a microgrid, is the charging efficiency of the battery;

[0158] Energy storage systems in multi-microgrid systems can alleviate the contradiction between renewable energy output and load imbalance. The spatiotemporal duality and source-load duality of energy storage systems can enhance the flexibility of microgrids and meet the dynamic change constraints of energy storage. The specific relationship is:

[0159]

[0160] in, 、 is the charging power and discharging power of the battery in the energy storage system at time t, 、 are the maximum charging power and maximum discharging power of the battery in the energy storage system, respectively. 、 is the charging and discharging efficiency of the energy storage system, 、 are the amount of electricity stored in the battery at time t and time t-1 respectively, 、 is the maximum and minimum storage capacity of the battery, 、 The amount of electricity stored in the battery at the initial and final moments. It is the charge and discharge status of the battery in the energy storage system at time t. It is a Boolean variable that can avoid simultaneous charging and discharging.

[0161] When the microgrid operates independently, the power of each microgrid is kept balanced within a unit time. The power balance constraint of each microgrid when operating independently satisfies the relationship:

[0162]

[0163] When multiple microgrids are operating in coordination, the power of each microgrid must be balanced within a unit time. The power balance constraints of the microgrids in coordinated operation satisfy:

[0164]

[0165] in, is the tth moment The actual electrical load of each microgrid;

[0166] No. The constraint conditions of a microgrid satisfy the relationship:

[0167]

[0168]

[0169]

[0170]

[0171]

[0172]

[0173] in, 、 For the The minimum power generation and maximum power generation of the gas turbine unit in each microgrid; 、 For the The minimum and maximum power generation of fuel cells in a microgrid; 、 The maximum output of the gas turbine unit that can be reduced or increased per unit time; For the The maximum amount of electricity purchased and sold between a microgrid and the main grid; The first The transaction status between a microgrid and the large grid is a Boolean variable, which prevents the microgrid and the large grid from purchasing and selling electricity at the same time.

[0174] In this embodiment, the multi-microgrid system takes three microgrids as an example. The three microgrids are respectively recorded as MG1, MG2 and MG3. The settings of relevant parameters of each microgrid are shown in Table 1.

[0175]

[0176] Secondly, a single microgrid participates in the coordinated operation of multiple microgrids as an independent rational individual. All participants hope to find a fairer way to distribute benefits through negotiation. Nash negotiation can meet the needs of multiple entities to the greatest extent. In addition, the solution that satisfies the maximization in Nash negotiation is the equilibrium solution to the problem. The standard model of Nash negotiation is:

[0177]

[0178] Where: For participants in negotiations economic benefits; For participants The economic benefits before participating in the cooperation, that is, the negotiation breakdown point, n is the total number of all participants in the negotiation; For participants Increased value of benefits gained through cooperation, To find the maximum function.

[0179] The issue of coordinated optimization of multiple microgrids in a multi-microgrid system focuses on operating costs, not benefits. Secondly, the lower the rigidity of each microgrid's scheduling of renewable energy consumption, the less unbalanced load demand response caused by differences in source-load characteristics, and the greater the overall contribution to the coordinated operation of the multi-microgrid system. Therefore, the standard Nash negotiation model is changed to an operating cost model for coordinated optimization of multiple microgrids. Using Nash negotiation theory, the relationship is:

[0180]

[0181] in, For the The operating cost of each microgrid when operating independently is the breaking point of the negotiation; To participate in the collaborative operation The operating cost of a microgrid, n is the number of all microgrids, is the maximum value function.

[0182] The Nash negotiation model is a non-convex nonlinear problem with multiple variables coupled. Therefore, the above model is decomposed into two sub-problems: the multi-microgrid collaborative operation cost minimization sub-problem P1 and the benefit distribution sub-problem P2, which are then solved in sequence. Secondly, when solving the multi-microgrid collaborative operation cost minimization sub-problem, the goal of the multi-microgrid collaborative optimization benefit maximization problem is to find the most satisfactory interactive power between each microgrid. In order to maximize the privacy of each microgrid, the Alternating Direction Method of Multipliers (ADMM) is used to solve sub-problem P1. By using the alternating direction multiplier method, the multi-microgrid collaborative optimization benefit maximization problem is converted into a total operation cost minimization problem, specifically:

[0183]

[0184] in, is the minimum function.

[0185] The specific solution steps of the ADMM algorithm are as follows:

[0186] (1) In this embodiment, the maximum number of iterations is set The number of iterations is 100. In the first iteration, the energy interaction between microgrids is 0, and the penalty factor is set to , the Lagrange multiplier is 0, and the first The augmented Lagrangian function of the minimum operating cost objective function of the microgrid;

[0187]

[0188] in, For the The sum of the Lagrangian augmented costs of the microgrids, To find the Lagrange multiplier for solving subproblem P1, The penalty factor for solving subproblem P1;

[0189] (2) Each microgrid updates its own transaction power strategy through local calculation, and only the transaction power information is exchanged between microgrids. Indicates the number of iterations. In each iteration, the following steps are performed:

[0190]

[0191] Other microgrids receive updated decision information To update its decision , specifically:

[0192]

[0193] in, For the The first iteration The microgrid transmits the data to the The amount of electricity exchanged between microgrids.

[0194] (3) After one round of iteration, update the Lagrange multiplier and the number of iterations;

[0195]

[0196] (4) Update the number of iterations h = h + 1;

[0197] (5) Determine whether the algorithm has reached convergence;

[0198]

[0199] If the algorithm converges, the iteration is terminated; otherwise, the iterative calculation is continued until the convergence condition is met.

[0200] It should be noted that the ADMM algorithm is a well-known technology and will not be described in detail here.

[0201] Therefore, through the above process, the optimal interactive power between each microgrid and the other microgrids can be obtained. Based on the optimal interactive power, the operating cost of each microgrid after participating in the coordinated operation is finally obtained;

[0202] After the above Nash negotiation, a fairer distribution of benefits among the microgrids in the multi-microgrid system has been obtained. However, there is still unfairness for the microgrids with greater contributions. Therefore, combined with the contribution factor, the Shapley value method is used to solve sub-problem P2, calculate the cost saving share of each microgrid, and redistribute the benefits of all microgrids participating in the collaborative operation on the basis of Nash negotiation, so that the benefit distribution value in the multi-microgrid system is equal to the sum of the marginal contribution values ​​of the microgrids participating in the collaborative operation. The specific formula is:

[0203] There are multiple alliance modes between each microgrid and other microgrids. For example, for the three microgrids in this embodiment, taking microgrid MG1 as an example, MG1 can form an alliance with MG2; MG1 can form an alliance with MG3; MG1 can form an alliance with MG2 and MG3;

[0204] All microgrids are grouped into a set N, and all non-empty subsets of the set N are obtained. All subsets containing the i-th microgrid are recorded as the set , will be collected Any element in is denoted as alliance S;

[0205] Since the above Nash negotiation is analyzed in one monitoring cycle, the modified Shapley value of the i-th microgrid in one monitoring cycle is:

[0206]

[0207] in, is the corrected Shapley value of the i-th microgrid, is the operating cost of the qth microgrid in the alliance S when it operates independently, is the total operating cost of all microgrids in alliance S, To save costs for Alliance S, The cost savings of removing the i-th microgrid for alliance S, is the cost saving of the i-th microgrid in alliance S, is the adjusted cost saving of the i-th microgrid in alliance S, is the number of all microgrids in the set N, is the number of microgrids in the alliance S, is the contribution factor of the i-th microgrid, represents the factorial symbol, Indicates belonging to the collection.

[0208] It should be noted that the Shapley value method is a well-known technology and will not be described in detail here. If the alliance S only contains one microgrid, the cost savings of the alliance S is 0. Secondly, for the multi-microgrid collaborative optimization operation strategy, the commercial solver CPLEX is called in the Matlab environment to solve the above process.

[0209] The cost savings are apportioned through the modified Shapley value of each microgrid, and then the benefits are distributed to different microgrids based on the total benefits after the alliance.

[0210] It should be understood that although Figure 1 The steps in the flowchart are shown in sequence as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. In addition, Figure 1At least part of the steps may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least part of the sub-steps or stages of other steps.

[0211] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0212] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the present application. It should be noted that a person skilled in the art can make various modifications and improvements without departing from the spirit of the present application. Therefore, any simple modifications, equivalent variations, and modifications to the above embodiments made in accordance with the technical essence of the present application without departing from the content of the present application's technical solution fall within the scope of protection of the present application's technical solution.

Claims

1. A multi-microgrid collaborative operation strategy based on Nash negotiation, characterized by: The strategy includes the following steps: The moment when the renewable energy power generation in each microgrid is not zero is recorded as the output moment; the discrete degree of all output moments of each microgrid is calculated and recorded as time dispersion; the sum of the discrete degree of active power on the load side of each microgrid at all times and the discrete degree of renewable energy power generation corresponding to all output moments is calculated; the ratio of time dispersion to the sum is recorded as the first stability, the power consumption on the load side at all times in each time period is trend decomposed, and the periodic intensity is calculated; the range of power consumption on the load side at all times in each time period is calculated and recorded as the first range; the first range and the periodic intensity of all time periods of each microgrid are merged to calculate the first abnormality of each microgrid; the normalized result of the ratio of the first abnormality to the first stability is recorded as the absorption evaluation value. Combined with the differences in the extreme changes in power consumption on the load side in different time periods and the periodic characteristics of power consumption on the load side, the absorption evaluation value of each microgrid is obtained; The recovery rate is calculated by analyzing the rate of change of the microgrid frequency on the power supply side of each microgrid from the extreme point to the rated frequency. The relevant conditions of the recovery rate and the difference between the power consumption on the load side and the power generation of renewable energy are analyzed to calculate the second stability. The response imbalance degree of each microgrid is obtained by combining the difference in the extreme changes of the power factor on the power supply side between adjacent time periods and the difference in the average level of the power factor. Combined with the absorption assessment value, the contribution factor of each microgrid is determined; a Nash negotiation model for the coordinated operation cost of multiple microgrids is constructed, and the objective function is established by minimizing the operation cost of the microgrid; the alternating direction multiplier method is used for iterative solution, and the cost of the microgrid is allocated by combining the contribution factor using the Shapley value method; The Nash negotiation model for constructing the multi-microgrid collaborative operation cost includes: in, is the operating cost of the i-th microgrid when it operates alone, that is, the negotiation breakdown point; C MG,i is the operating cost of the i-th microgrid after participating in the collaborative operation, n is the number of all microgrids, and max is the maximum value function; The objective function is: min C MG,i =C net,i +C GT,i +C FC,i +C tol,i +C PV,i +C WT,i +C CO2,i +C SOC,i , where C MG,i is the operating cost of the i-th microgrid, C GT,i 、C FC,i 、C PV,i 、C WT,i are the operating costs of the gas turbine unit, fuel cell, photovoltaic power generation, and wind turbine power generation equipment in the i-th microgrid, respectively. net,i is the interaction power cost between the i-th microgrid and the large grid, C tol,i is the total interaction cost between the i-th microgrid and all other microgrids, C CO2,i is the carbon trading cost of the i-th microgrid, C SOC,i is the operating cost of the i-th microgrid and energy storage system for electric energy cooperation, min C MG,i Indicates the minimum operating cost of the i-th microgrid; The cost allocation of the microgrid using the Shapley value method includes: All microgrids are grouped into set N, and all non-empty subsets of set N are obtained. All subsets containing the i-th microgrid are grouped into set M. i , the set M i Any element in is denoted as alliance S; The corrected Shapley value of the i-th microgrid for: in, is the operating cost of the qth microgrid in the alliance S when it operates independently, C S is the total operating cost of all microgrids in alliance S, V(S) is the cost savings of alliance S, V(S / {i}) is the cost savings of alliance S excluding the i-th microgrid, Δδ i is the cost saving of the i-th microgrid in the alliance S, Δδ′ i is the adjusted cost saving of the i-th microgrid in the alliance S, n is the number of all microgrids in the set N, |S| is the number of microgrids in the alliance S, g i is the contribution factor of the i-th microgrid, ! represents the factorial symbol, ∈ represents the number of items in the set.

2. The multi-microgrid collaborative operation strategy based on Nash negotiation according to claim 1, characterized in that: The first abnormality degree is the product of the mean of the first range in all time periods of each microgrid and the mean of the periodicity intensity in all time periods.

3. The multi-microgrid coordinated operation strategy based on Nash negotiation according to claim 1, characterized in that: The calculation process of the recovery rate is: Obtain the extreme points of the microgrid frequency at all times under each microgrid, including the maximum and minimum points; record the difference between the extreme value corresponding to each extreme point and the rated frequency as the relative deviation; Calculate the time interval between the corresponding moment of each extreme point and the corresponding moment when the microgrid frequency gradually decreases to the rated frequency; The ratio of the relative deviation to the time interval is used as the recovery rate of each extreme point.

4. The multi-microgrid collaborative operation strategy based on Nash negotiation according to claim 3, characterized in that: The calculating the second stability comprises: Calculating the autocorrelation coefficients of the recovery rates of all maximum points of each microgrid; calculating the autocorrelation coefficients of the recovery rates of all minimum points of each microgrid; The sum of all autocorrelation coefficients corresponding to the maximum point and the autocorrelation coefficient corresponding to the minimum point under each microgrid is taken as the correlation degree of each microgrid; Calculate the cumulative sum of the differences between the load-side power consumption and renewable energy generation of each microgrid at each moment; The second stability is a ratio of the correlation to the cumulative sum.

5. The multi-microgrid collaborative operation strategy based on Nash negotiation according to claim 1, characterized in that: Obtaining the response imbalance degree of each microgrid includes: Calculate the range of the power factor on the power supply side of each microgrid at all times in each time period, and record it as the second range difference; record the sum of the differences of the second range differences between all two adjacent time periods in each microgrid as the relative difference; Calculate the average power factor of each microgrid at all times in each time period on the power supply side; record the sum of the differences between the average values ​​of all two adjacent time periods in each microgrid as the average difference; fusing the average difference and the relative difference to calculate a second abnormality degree of each microgrid; The response imbalance degree is a normalized result of a ratio of the second abnormality degree to the second stability degree.

6. The multi-microgrid coordinated operation strategy based on Nash negotiation according to claim 5, characterized in that: The second abnormality degree is the sum of a result of positive mapping of the average difference and a result of positive mapping of the relative difference.

7. The multi-microgrid coordinated operation strategy based on Nash negotiation according to claim 1, characterized in that: The process of obtaining the contribution factor is: comprehensively evaluating the response imbalance and the absorption evaluation value of all microgrids, and calculating the comprehensive score of each microgrid; the contribution factor is the comprehensive score.

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